BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, Cao K, Liu D, Wang G, Xu Q, Fang X, Zhang S, Xia J. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Radiology. 2020;296:E65-E71. [PMID: 32191588 DOI: 10.1148/radiol.2020200905] [Cited by in Crossref: 972] [Cited by in F6Publishing: 1033] [Article Influence: 324.0] [Reference Citation Analysis]
Number Citing Articles
1 Newell JD. Using Limited Memory Lung CT AI to Derive Advanced Quantitative CT Lung Metrics of COPD, ILD, and COVID-19 Pneumonia. Developing the Digital Lung 2024. [DOI: 10.1016/b978-0-323-79501-2.00007-6] [Reference Citation Analysis]
2 Parvaiz A, Khalid MA, Zafar R, Ameer H, Ali M, Fraz MM. Vision Transformers in medical computer vision—A contemplative retrospection. Engineering Applications of Artificial Intelligence 2023;122:106126. [DOI: 10.1016/j.engappai.2023.106126] [Reference Citation Analysis]
3 Wang X, Cheng L, Zhang D, Liu Z, Jiang L. Broad learning solution for rapid diagnosis of COVID-19. Biomed Signal Process Control 2023;83:104724. [PMID: 36811035 DOI: 10.1016/j.bspc.2023.104724] [Reference Citation Analysis]
4 Bhosale YH, Patnaik KS. Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review. Multimed Tools Appl 2023. [DOI: 10.1007/s11042-023-15029-1] [Reference Citation Analysis]
5 Godoy MFD, Chatkin JM, Rodrigues RS, Forte GC, Marchiori E, Gavenski N, Barros RC, Hochhegger B. Artificial intelligence to predict the need for mechanical ventilation in cases of severe COVID-19. Radiol Bras 2023. [DOI: 10.1590/0100-3984.2022.0049] [Reference Citation Analysis]
6 Mlynska L, Malouhi A, Ingwersen M, Güttler F, Gräger S, Teichgräber U. Artificial intelligence for assistance of radiology residents in chest CT evaluation for COVID-19 pneumonia: a comparative diagnostic accuracy study. Acta Radiol 2023;:2841851231162085. [PMID: 36890698 DOI: 10.1177/02841851231162085] [Reference Citation Analysis]
7 Mouna Afif, Riadh Ayachi, Yahia Said, Mohamed Atri. Deep learning-based technique for lesions segmentation in CT scan images for COVID-19 prediction. Multimed Tools Appl 2023. [ DOI: 10.1007/s11042-023-14941-w] [Reference Citation Analysis]
8 Amit K. Shukla, Taniya Seth, Pranab K. Muhuri. Artificial intelligence centric scientific research on COVID-19: an analysis based on scientometrics data. Multimed Tools Appl 2023. [ DOI: 10.1007/s11042-023-14642-4] [Reference Citation Analysis]
9 Keerthana R, Gladston A, Nehemiah HK. Transfer learning-based CNN diagnostic framework for diagnosis of COVID-19 from lung CT images. The Imaging Science Journal 2023. [DOI: 10.1080/13682199.2023.2170768] [Reference Citation Analysis]
10 Kavuran G, Gökhan Ş, Yeroğlu C. COVID-19 and human development: An approach for classification of HDI with deep CNN. Biomed Signal Process Control 2023;81:104499. [PMID: 36530217 DOI: 10.1016/j.bspc.2022.104499] [Reference Citation Analysis]
11 Al-Naser YA. The impact of artificial intelligence on radiography as a profession: A narrative review. J Med Imaging Radiat Sci 2023;54:162-6. [PMID: 36376210 DOI: 10.1016/j.jmir.2022.10.196] [Reference Citation Analysis]
12 da Silveira TLT, Pinto PGL, Lermen TS, Jung CR. Omnidirectional 2.5D representation for COVID-19 diagnosis using chest CTs. J Vis Commun Image Represent 2023;91:103775. [PMID: 36741546 DOI: 10.1016/j.jvcir.2023.103775] [Reference Citation Analysis]
13 Faiq M, Geok Tan K, Pao Liew C, Hossain F, Tso C, Li Lim L, Khang Wong AY, Mohd Shah Z. Prediction of energy consumption in campus buildings using long short-term memory. Alexandria Engineering Journal 2023;67:65-76. [DOI: 10.1016/j.aej.2022.12.015] [Reference Citation Analysis]
14 Warin K, Limprasert W, Suebnukarn S, Paipongna T, Jantana P, Vicharueang S. Maxillofacial fracture detection and classification in computed tomography images using convolutional neural network-based models. Sci Rep 2023;13:3434. [PMID: 36859660 DOI: 10.1038/s41598-023-30640-w] [Reference Citation Analysis]
15 Wu Y, Qi Q, Qi S, Yang L, Wang H, Yu H, Li J, Wang G, Zhang P, Liang Z, Chen R. Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans. Comput Biol Med 2023;154:106567. [PMID: 36738705 DOI: 10.1016/j.compbiomed.2023.106567] [Reference Citation Analysis]
16 Gazeau S, Deng X, Ooi HK, Mostefai F, Hussin J, Heffernan J, Jenner AL, Craig M. The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions. Immunoinformatics (Amst) 2023;9:100021. [PMID: 36643886 DOI: 10.1016/j.immuno.2023.100021] [Reference Citation Analysis]
17 Bezbaruah R, Ghosh M, Kumari S, Nongrang L, Ali SR, Lahiri M, Waris H, Kakoti BB. Role of AI and ML in Epidemics and Pandemics. Bioinformatics Tools for Pharmaceutical Drug Product Development 2023. [DOI: 10.1002/9781119865728.ch15] [Reference Citation Analysis]
18 Jeevitha S, Valarmathi K. A joint segmentation and classification framework for COVID‐19 infection segmentation and detection from chest CT images. Int J Imaging Syst Tech 2023. [DOI: 10.1002/ima.22862] [Reference Citation Analysis]
19 Kurt Z, Işık Ş, Kaya Z, Anagün Y, Koca N, Çiçek S. Evaluation of EfficientNet models for COVID-19 detection using lung parenchyma. Neural Comput Appl 2023;:1-12. [PMID: 36843903 DOI: 10.1007/s00521-023-08344-z] [Reference Citation Analysis]
20 Shrivastav LK, Kumar R. Empirical analysis of impact of weather and air pollution parameters on COVID-19 spread and control in India using Machine Learning Algorithm.. [DOI: 10.21203/rs.3.rs-1997309/v1] [Reference Citation Analysis]
21 Mijanur Rahman M, Khatun F. Challenges and Prospective of AI and 5G-Enabled Technologies in Emerging Applications during the Pandemic. Artificial Intelligence 2023. [DOI: 10.5772/intechopen.109450] [Reference Citation Analysis]
22 Tzeng IS, Hsieh PC, Su WL, Hsieh TH, Chang SC. Artificial Intelligence-Assisted Chest X-ray for the Diagnosis of COVID-19: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2023;13. [PMID: 36832072 DOI: 10.3390/diagnostics13040584] [Reference Citation Analysis]
23 Malik H, Anees T, Naeem A, Naqvi RA, Loh WK. Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans. Bioengineering (Basel) 2023;10. [PMID: 36829697 DOI: 10.3390/bioengineering10020203] [Reference Citation Analysis]
24 Zhang B, Ming C. Digital Transformation and Open Innovation Planning of Response to COVID-19 Outbreak: A Systematic Literature Review and Future Research Agenda. Int J Environ Res Public Health 2023;20. [PMID: 36768096 DOI: 10.3390/ijerph20032731] [Reference Citation Analysis]
25 Ahamed MKU, Islam MM, Uddin MA, Akhter A, Acharjee UK, Paul BK, Moni MA. DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images. Diagnostics (Basel) 2023;13. [PMID: 36766662 DOI: 10.3390/diagnostics13030551] [Reference Citation Analysis]
26 Silva LDJ, Cortes O, Diniz J. A novel ensemble CNN model for COVID-19 classification in computerized tomography scans. Results in Control and Optimization 2023. [DOI: 10.1016/j.rico.2023.100215] [Reference Citation Analysis]
27 Fontes C, Corrigan C, Lütge C. Governing AI during a pandemic crisis: Initiatives at the EU level. Technol Soc 2023;72:102204. [PMID: 36777094 DOI: 10.1016/j.techsoc.2023.102204] [Reference Citation Analysis]
28 Aslani S, Jacob J. Utilisation of deep learning for COVID-19 diagnosis. Clin Radiol 2023;78:150-7. [PMID: 36639173 DOI: 10.1016/j.crad.2022.11.006] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
29 Meng Y, Bridge J, Addison C, Wang M, Merritt C, Franks S, Mackey M, Messenger S, Sun R, Fitzmaurice T, McCann C, Li Q, Zhao Y, Zheng Y. Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning. Med Image Anal 2023;84:102722. [PMID: 36574737 DOI: 10.1016/j.media.2022.102722] [Reference Citation Analysis]
30 Gupta K, Bajaj V. Deep learning models-based CT-scan image classification for automated screening of COVID-19. Biomed Signal Process Control 2023;80:104268. [PMID: 36267466 DOI: 10.1016/j.bspc.2022.104268] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
31 Gao CA, Pickens CI, Morales-Nebreda L, Wunderink RG. Clinical Features of COVID-19 and Differentiation from Other Causes of CAP. Semin Respir Crit Care Med 2023;44:8-20. [PMID: 36646082 DOI: 10.1055/s-0042-1759889] [Reference Citation Analysis]
32 Wen C, Liu S, Liu S, Heidari AA, Hijji M, Zarco C, Muhammad K. ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans. Comput Biol Med 2023;153:106338. [PMID: 36640529 DOI: 10.1016/j.compbiomed.2022.106338] [Reference Citation Analysis]
33 Sailunaz K, Özyer T, Rokne J, Alhajj R. A survey of machine learning-based methods for COVID-19 medical image analysis. Med Biol Eng Comput 2023;:1-41. [PMID: 36707488 DOI: 10.1007/s11517-022-02758-y] [Reference Citation Analysis]
34 Lee Y, Kim YS, Lee DI, Jeong S, Kang GH, Jang YS, Kim W, Choi HY, Kim JG. Comparison of the Diagnostic Performance of Deep Learning Algorithms for Reducing the Time Required for COVID-19 RT-PCR Testing. Viruses 2023;15. [PMID: 36851519 DOI: 10.3390/v15020304] [Reference Citation Analysis]
35 R. M, Sundar Rao P, H. A, Asirvadam VS. Hybrid Deep Learning Models for Effective COVID -19 Diagnosis with Chest X-Rays. Structural and Functional Aspects of Biocomputing Systems for Data Processing 2023. [DOI: 10.4018/978-1-6684-6523-3.ch005] [Reference Citation Analysis]
36 Topff L, Groot Lipman KBW, Guffens F, Wittenberg R, Bartels-Rutten A, van Veenendaal G, Hess M, Lamerigts K, Wakkie J, Ranschaert E, Trebeschi S, Visser JJ, Beets-Tan RGH; ICOVAI, International Consortium for COVID-19 Imaging AI. Is the generalizability of a developed artificial intelligence algorithm for COVID-19 on chest CT sufficient for clinical use? Results from the International Consortium for COVID-19 Imaging AI (ICOVAI). Eur Radiol 2023;:1-10. [PMID: 36651954 DOI: 10.1007/s00330-022-09303-3] [Reference Citation Analysis]
37 Vinod DN, Prabaharan SRS. COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled. Arch Comput Methods Eng 2023;:1-16. [PMID: 36685135 DOI: 10.1007/s11831-023-09882-4] [Reference Citation Analysis]
38 Wang SH, Satapathy SC, Xie MX, Zhang YD. ELUCNN for explainable COVID-19 diagnosis. Soft comput 2023;:1-17. [PMID: 36686545 DOI: 10.1007/s00500-023-07813-w] [Reference Citation Analysis]
39 Alhares H, Tanha J, Balafar MA. AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19. Evolving Systems 2023. [DOI: 10.1007/s12530-023-09484-2] [Reference Citation Analysis]
40 Khan A, Khan SH, Saif M, Batool A, Sohail A, Waleed Khan M. A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron. Journal of Experimental & Theoretical Artificial Intelligence 2023. [DOI: 10.1080/0952813x.2023.2165724] [Reference Citation Analysis]
41 Almotairi KH, Hussein AM, Abualigah L, Abujayyab SKM, Mahmoud EH, Ghanem BO, Gandomi AH. Impact of Artificial Intelligence on COVID-19 Pandemic: A Survey of Image Processing, Tracking of Disease, Prediction of Outcomes, and Computational Medicine. BDCC 2023;7:11. [DOI: 10.3390/bdcc7010011] [Reference Citation Analysis]
42 Jeong YJ, Wi YM, Park H, Lee JE, Kim SH, Lee KS. Current and Emerging Knowledge in COVID-19. Radiology 2023;306:e222462. [PMID: 36625747 DOI: 10.1148/radiol.222462] [Reference Citation Analysis]
43 Nishino M, Schiebler ML. Advances in Thoracic Imaging: Key Developments in the Past Decade and Future Directions. Radiology 2023;306:e222536. [PMID: 36625742 DOI: 10.1148/radiol.222536] [Reference Citation Analysis]
44 Mercaldo F, Belfiore MP, Reginelli A, Brunese L, Santone A. Coronavirus covid-19 detection by means of explainable deep learning. Sci Rep 2023;13:462. [PMID: 36627339 DOI: 10.1038/s41598-023-27697-y] [Reference Citation Analysis]
45 Mukanhaire L, Li H, Fan Z, Yang L, Zheng Y, Ran Z, Zong X, Zhang L, Gong Y, Yang C, Gong J. Efficacy of corticosteroids as an adjunctive therapy in the treatment of community-acquired pneumonia: a systematic review and meta-analysis. Acta Materia Medica 2023;2. [DOI: 10.15212/amm-2022-0037] [Reference Citation Analysis]
46 Hou J, Xu J, Zhang N, Wang Y, Zhang Y, Zhang X, Feng R. CMC_v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors. Lecture Notes in Computer Science 2023. [DOI: 10.1007/978-3-031-25082-8_32] [Reference Citation Analysis]
47 Islam MM, Hannan T, Sarker L, Ahmed Z. COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images. Proceedings of International Conference on Data Science and Applications 2023. [DOI: 10.1007/978-981-19-6634-7_28] [Reference Citation Analysis]
48 Karaddi SH, Sharma LD. Automated multi-class classification of lung diseases from CXR-images using pre-trained convolutional neural networks. Expert Systems with Applications 2023;211:118650. [DOI: 10.1016/j.eswa.2022.118650] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
49 Rahman F, Ahmad M. Detection of the Most Essential Characteristics from Blood Routine Tests to Increase COVID-19 Diagnostic Capacity by Using Machine Learning Algorithms. Proceedings of International Conference on Information and Communication Technology for Development 2023. [DOI: 10.1007/978-981-19-7528-8_5] [Reference Citation Analysis]
50 Jyoti K, Sushma S, Yadav S, Kumar P, Pachori RB, Mukherjee S. Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images. Comput Biol Med 2023;152:106331. [PMID: 36502692 DOI: 10.1016/j.compbiomed.2022.106331] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
51 Alhadad AA, Mostafa RR, El-Bakry HM. Deep SVDD and Transfer Learning for COVID-19 Diagnosis Using CT Images. Comput Intell Neurosci 2023;2023:6070970. [PMID: 36926185 DOI: 10.1155/2023/6070970] [Reference Citation Analysis]
52 Sharma J, Vinod DF. Deep CNN Model Embedded with Inception Layers for COVID-19 Classification. ICT with Intelligent Applications 2023. [DOI: 10.1007/978-981-19-3571-8_42] [Reference Citation Analysis]
53 Chen H, Jiang Y, Ko H, Loew M. A teacher–student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT images. Biomedical Signal Processing and Control 2023;79:104250. [DOI: 10.1016/j.bspc.2022.104250] [Reference Citation Analysis]
54 Ballari GS, Giraddi S, Chickerur S, Kanakareddi S. An Explainable AI-Based Skin Disease Detection. ICT Infrastructure and Computing 2023. [DOI: 10.1007/978-981-19-5331-6_30] [Reference Citation Analysis]
55 Abdulkhaleq MT, Rashid TA, Hassan BA, Alsadoon A, Bacanin N, Chhabra A, Vimal S. Fitness dependent optimizer with neural networks for COVID-19 patients. Comput Methods Programs Biomed Update 2023;3:100090. [PMID: 36591535 DOI: 10.1016/j.cmpbup.2022.100090] [Reference Citation Analysis]
56 Varshney A, Subasi A. A deep learning approach for COVID-19 detection from computed tomography scans. Applications of Artificial Intelligence in Medical Imaging 2023. [DOI: 10.1016/b978-0-443-18450-5.00011-6] [Reference Citation Analysis]
57 Ghashghaei S, Wood DA, Sadatshojaei E, Jalilpoor M. Grayscale Image Statistical Attributes Effectively Distinguish the Severity of Lung Abnormalities in CT Scan Slices of COVID-19 Patients. SN Comput Sci 2023;4:201. [PMID: 36789248 DOI: 10.1007/s42979-022-01642-8] [Reference Citation Analysis]
58 Han D, Chen Y, Li X, Li W, Zhang X, He T, Yu Y, Dou Y, Duan H, Yu N. Development and validation of a 3D-convolutional neural network model based on chest CT for differentiating active pulmonary tuberculosis from community-acquired pneumonia. Radiol Med 2023;128:68-80. [PMID: 36574111 DOI: 10.1007/s11547-022-01580-8] [Reference Citation Analysis]
59 Kamalov F, Cherukuri AK, Sulieman H, Thabtah F, Hossain A. Machine learning applications for COVID-19: a state-of-the-art review. Data Science for Genomics 2023. [DOI: 10.1016/b978-0-323-98352-5.00010-0] [Reference Citation Analysis]
60 Liao H, Zhou SK, Luo J. Challenges and future directions. Deep Network Design for Medical Image Computing 2023. [DOI: 10.1016/b978-0-12-824383-1.00019-8] [Reference Citation Analysis]
61 Takateyama Y, Haruishi T, Hashimoto M, Otake Y, Akashi T, Shimizu A. Attention induction for a CT volume classification of COVID-19. Int J Comput Assist Radiol Surg 2023;18:289-301. [PMID: 36251150 DOI: 10.1007/s11548-022-02769-y] [Reference Citation Analysis]
62 Dommeti D, Nallapati SRK, Srinivas PVVS, Mandhala VN. Repercussions of Incorporating Filters in CNN Model to Boost the Diagnostic Ability of SARS-CoV-2 Virus Using Chest Computed Tomography Scans. Smart Technologies in Data Science and Communication 2023. [DOI: 10.1007/978-981-19-6880-8_22] [Reference Citation Analysis]
63 Velu S. An efficient, lightweight MobileNetV2-based fine-tuned model for COVID-19 detection using chest X-ray images. MBE 2023;20:8400-8427. [DOI: 10.3934/mbe.2023368] [Reference Citation Analysis]
64 Jeyakumar V, Sundaram P, Ramapathiran N. Artificial Intelligence-Based Predictive Tools for Life-Threatening Diseases. System Design for Epidemics Using Machine Learning and Deep Learning 2023. [DOI: 10.1007/978-3-031-19752-9_8] [Reference Citation Analysis]
65 Bakshi S, Palit S, Bhattacharya U, Gholami K, Hussain N, Mitra D. A Novel CNN-Based Approach for Distinguishing Between COVID and Common Pneumonia. Image and Vision Computing 2023. [DOI: 10.1007/978-3-031-25825-1_24] [Reference Citation Analysis]
66 Manigandan S, Praveenkumar TR, Brindhadevi K. A review on role of nitrous oxide nanoparticles, potential vaccine targets, drug, health care and artificial intelligence to combat COVID-19. Appl Nanosci 2023;13:111-8. [PMID: 34150443 DOI: 10.1007/s13204-021-01935-z] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
67 Ur Rehman S, Shafqat F, Niaz K. Recent artificial intelligence methods and coronaviruses. Application of Natural Products in SARS-CoV-2 2023. [DOI: 10.1016/b978-0-323-95047-3.00009-5] [Reference Citation Analysis]
68 Jasmine Pemeena Priyadarsini M, Kotecha K, Rajini GK, Hariharan K, Utkarsh Raj K, Bhargav Ram K, Indragandhi V, Subramaniyaswamy V, Pandya S. Lung Diseases Detection Using Various Deep Learning Algorithms. J Healthc Eng 2023;2023:3563696. [PMID: 36776955 DOI: 10.1155/2023/3563696] [Reference Citation Analysis]
69 Pazooki B, Ahangari A, Mehrabi Nejad MM, Batavani N, Salahshour F. Evaluation of Follow-Up CT Scans in Patients with Severe Initial Pulmonary Involvement by COVID-19. Can Respir J 2022;2022:6972998. [PMID: 36618585 DOI: 10.1155/2022/6972998] [Reference Citation Analysis]
70 Bhatele KR, Jha A, Tiwari D, Bhatele M, Sharma S, Mithora MR, Singhal S. COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans. Cognit Comput 2022;:1-38. [PMID: 36593991 DOI: 10.1007/s12559-022-10076-6] [Reference Citation Analysis]
71 Al-Shourbaji I, Kachare PH, Abualigah L, Abdelhag ME, Elnaim B, Anter AM, Gandomi AH. A Deep Batch Normalized Convolution Approach for Improving COVID-19 Detection from Chest X-ray Images. Pathogens 2022;12. [PMID: 36678365 DOI: 10.3390/pathogens12010017] [Reference Citation Analysis]
72 Benmalek E, El Mhamdi J, Jilbab A, Jbari A. A COUGH-BASED COVID-19 DETECTION SYSTEM USING PCA AND MACHINE LEARNING CLASSIFIERS. acs 2022;18:96-115. [DOI: 10.35784/acs-2022-31] [Reference Citation Analysis]
73 Fanyang Meng, Jonathan Kottlors, Rahil Shahzad, Haifeng Liu, Philipp Fervers, Yinhua Jin, Miriam Rinneburger, Dou Le, Mathilda Weisthoff, Wenyun Liu, Mengzhe Ni, Ye Sun, Liying An, Xiaochen Huai, Dorottya Móré, Athanasios Giannakis, Isabel Kaltenborn, Andreas Bucher, David Maintz, Lei Zhang, Frank Thiele, Mingyang Li, Michael Perkuhn, Huimao Zhang, Thorsten Persigehl. AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study. Eur Radiol 2022. [PMID: 36525088 DOI: 10.1007/s00330-022-09335-9] [Reference Citation Analysis]
74 Uddin KMM, Dey SK, Babu HMH, Mostafiz R, Uddin S, Shoombuatong W, Moni MA. Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images. Sci Rep 2022;12:21796. [PMID: 36526680 DOI: 10.1038/s41598-022-25539-x] [Reference Citation Analysis]
75 Hertel R, Benlamri R. Deep Learning Techniques for COVID-19 Diagnosis and Prognosis Based on Radiological Imaging. ACM Comput Surv 2022. [DOI: 10.1145/3576898] [Reference Citation Analysis]
76 Eappen P, Olujinmi TD. Telemedicine and Digital Public Health in Pandemic Times. Health Informatics and Patient Safety in Times of Crisis 2022. [DOI: 10.4018/978-1-6684-5499-2.ch007] [Reference Citation Analysis]
77 Strang KD. How Could Machine Learning Help Healthcare Informatics Predict Coronavirus? Health Informatics and Patient Safety in Times of Crisis 2022. [DOI: 10.4018/978-1-6684-5499-2.ch002] [Reference Citation Analysis]
78 Sciarretta E, Mancini R, Greco E. Artificial Intelligence for Healthcare and Social Services: Optimizing Resources and Promoting Sustainability. Sustainability 2022;14:16464. [DOI: 10.3390/su142416464] [Reference Citation Analysis]
79 Asif S, Wenhui Y, Jinhai S, Waheed Z, Yueyang Y, Jin H. CVD19-Net: An Automated Deep Learning Model for COVID-19 Screening using Chest CT Images. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2022. [DOI: 10.1109/bibm55620.2022.9995590] [Reference Citation Analysis]
80 Baeza S, Gil D, Garcia-Olivé I, Salcedo-Pujantell M, Deportós J, Sanchez C, Torres G, Moragas G, Rosell A. A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients. EJNMMI Phys 2022;9:84. [PMID: 36469151 DOI: 10.1186/s40658-022-00510-x] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
81 Kato S, Oda M, Mori K, Shimizu A, Otake Y, Hashimoto M, Akashi T, Hotta K. Classification and visual explanation for COVID-19 pneumonia from CT images using triple learning. Sci Rep 2022;12:20840. [PMID: 36460708 DOI: 10.1038/s41598-022-24936-6] [Reference Citation Analysis]
82 Alduaiji N, Algarni A, Abdalaha Hamza S, Abdel Azim G, Hamam H. A Lightweight CNN and Class Weight Balancing on Chest X-ray Images for COVID-19 Detection. Electronics 2022;11:4008. [DOI: 10.3390/electronics11234008] [Reference Citation Analysis]
83 Walston SL, Matsumoto T, Miki Y, Ueda D. Artificial intelligence-based model for COVID-19 prognosis incorporating chest radiographs and clinical data; a retrospective model development and validation study. BJR 2022;95. [DOI: 10.1259/bjr.20220058] [Reference Citation Analysis]
84 Anton J, Castelli L, Chan MF, Outters M, Tang WH, Cheung V, Shukla P, Walambe R, Kotecha K. How Well Do Self-Supervised Models Transfer to Medical Imaging? J Imaging 2022;8. [PMID: 36547485 DOI: 10.3390/jimaging8120320] [Reference Citation Analysis]
85 Galzin E, Roche L, Vlachomitrou A, Nempont O, Carolus H, Schmidt-richberg A, Jin P, Rodrigues P, Klinder T, Richard J, Tazarourte K, Douplat M, Sigal A, Bouscambert-duchamp M, Si-mohamed SA, Gouttard S, Mansuy A, Talbot F, Pialat J, Rouvière O, Milot L, Cotton F, Douek P, Duclos A, Rabilloud M, Boussel L. Additional value of chest CT AI-based quantification of lung involvement in predicting death and ICU admission for COVID-19 patients. Research in Diagnostic and Interventional Imaging 2022;4:100018. [DOI: 10.1016/j.redii.2022.100018] [Reference Citation Analysis]
86 Reis HC, Turk V. COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images. Artif Intell Med 2022;134:102427. [PMID: 36462906 DOI: 10.1016/j.artmed.2022.102427] [Reference Citation Analysis]
87 Di Napoli A, Tagliente E, Pasquini L, Cipriano E, Pietrantonio F, Ortis P, Curti S, Boellis A, Stefanini T, Bernardini A, Angeletti C, Ranieri SC, Franchi P, Voicu IP, Capotondi C, Napolitano A. 3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients. J Digit Imaging 2022. [PMID: 36450922 DOI: 10.1007/s10278-022-00734-4] [Reference Citation Analysis]
88 Hilal W, Chislett MG, Snider B, Mcbean EA, Yawney J, Gadsden SA. Use of AI to assess COVID-19 variant impacts on hospitalization, ICU, and death. Front Artif Intell 2022;5. [DOI: 10.3389/frai.2022.927203] [Reference Citation Analysis]
89 Bekkouche A,, Merzoug M, Hadjila F, Bellaouedj I,, Etchiali A. Automatic Diagnosis of Pneumonia and COVID-19 Using Convolutional Neural Networks and Transfer Learning. International Journal of Neural Networks and Advanced Applications 2022;9:40-48. [DOI: 10.46300/91016.2022.9.7] [Reference Citation Analysis]
90 Farooq U, Amtullah A, Rehman A, Sarfraz M. Lightweight Cost Effective Deep Learning Model for COVID-19 Detection using CXR Images. 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT) 2022. [DOI: 10.1109/impact55510.2022.10029060] [Reference Citation Analysis]
91 Ozsahin DU, Isa NA, Uzun B. The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis. Diagnostics (Basel) 2022;12. [PMID: 36552949 DOI: 10.3390/diagnostics12122943] [Reference Citation Analysis]
92 Duong LT, Nguyen PT, Iovino L, Flammini M. Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning. Applied Soft Computing 2022. [DOI: 10.1016/j.asoc.2022.109851] [Reference Citation Analysis]
93 Dubey AK, Mohbey KK. Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images. New Gener Comput 2022. [DOI: 10.1007/s00354-022-00195-x] [Reference Citation Analysis]
94 Lee KW, Chin RKY. Diverse COVID-19 CT Image-to-Image Translation with Stacked Residual Dropout. Bioengineering 2022;9:698. [DOI: 10.3390/bioengineering9110698] [Reference Citation Analysis]
95 Ubale Kiru M, Belaton B, Chew X, Almotairi KH, Hussein AM, Aminu M. Comparative analysis of some selected generative adversarial network models for image augmentation: a case study of COVID-19 x-ray and CT images. IFS 2022;43:7153-7172. [DOI: 10.3233/jifs-220017] [Reference Citation Analysis]
96 Fatema K, Montaha S, Rony MAH, Azam S, Hasan MZ, Jonkman M. A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images. Biomedicines 2022;10:2835. [DOI: 10.3390/biomedicines10112835] [Reference Citation Analysis]
97 Li G, Sun C, Sun Z. A Deep Learning Based Method For COVID-19 Classification Using Chest CT Images. 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) 2022. [DOI: 10.1109/cisp-bmei56279.2022.9980099] [Reference Citation Analysis]
98 Sun W, Pang Y, Zhang G. CCT: Lightweight compact convolutional transformer for lung disease CT image classification. Front Physiol 2022;13. [DOI: 10.3389/fphys.2022.1066999] [Reference Citation Analysis]
99 Nalluri S, Sasikala R. A deep neural architecture for SOTA pneumonia detection from chest X-rays. Int J Syst Assur Eng Manag 2022. [DOI: 10.1007/s13198-022-01788-x] [Reference Citation Analysis]
100 Kim K, Lee MK, Shin HK, Lee H, Kim B, Kang S. Development and application of survey-based artificial intelligence for clinical decision support in managing infectious diseases: A pilot study on a hospital in central Vietnam. Front Public Health 2022;10:1023098. [PMID: 36438286 DOI: 10.3389/fpubh.2022.1023098] [Reference Citation Analysis]
101 Luo J, Sun Y, Chi J, Liao X, Xu C. A novel deep learning-based method for COVID-19 pneumonia detection from CT images. BMC Med Inform Decis Mak 2022;22:284. [PMID: 36324135 DOI: 10.1186/s12911-022-02022-1] [Reference Citation Analysis]
102 Alshmrani GMM, Ni Q, Jiang R, Pervaiz H, Elshennawy NM. A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images. Alexandria Engineering Journal 2022. [DOI: 10.1016/j.aej.2022.10.053] [Reference Citation Analysis]
103 Ufuk F, Savaş R. COVID-19 pneumonia: lessons learned, challenges, and preparing for the future. Diagn Interv Radiol 2022;28:576-85. [PMID: 36550758 DOI: 10.5152/dir.2022.221881] [Reference Citation Analysis]
104 Baruah D, Runge L, Jones RH, Collins HR, Kabakus IM, McBee MP. COVID-19 Diagnosis on Chest Radiograph Using Artificial Intelligence. Cureus 2022;14:e31897. [PMID: 36579217 DOI: 10.7759/cureus.31897] [Reference Citation Analysis]
105 Chakraborty S, Mali K. SUFEMO: A superpixel based fuzzy image segmentation method for COVID-19 radiological image elucidation. Appl Soft Comput 2022;129:109625. [PMID: 36124000 DOI: 10.1016/j.asoc.2022.109625] [Reference Citation Analysis]
106 Akanji W, Okey O, Adelanwa S, Odesanya O, Olaleye T, Amusu M, Akinrinlola A, Oladejo A. A blind steganalysis-based predictive analytics of numeric image descriptors for digital forensics with Random Forest & SqueezeNet. 2022 5th Information Technology for Education and Development (ITED) 2022. [DOI: 10.1109/ited56637.2022.10051337] [Reference Citation Analysis]
107 Wang Y, Hargreaves CA. A Review Study of the Deep Learning Techniques used for the Classification of Chest Radiological Images for COVID-19 Diagnosis. International Journal of Information Management Data Insights 2022;2:100100. [DOI: 10.1016/j.jjimei.2022.100100] [Reference Citation Analysis]
108 Alqahtani A, Zahoor MM, Nasrullah R, Fareed A, Cheema AA, Shahrose A, Irfan M, Alqhatani A, Alsulami AA, Zaffar M, Rahman S. Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images. Life 2022;12:1709. [DOI: 10.3390/life12111709] [Reference Citation Analysis]
109 Addo D, Zhou S, Jackson JK, Nneji GU, Monday HN, Sarpong K, Patamia RA, Ekong F, Owusu-agyei CA. EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images. Diagnostics 2022;12:2569. [DOI: 10.3390/diagnostics12112569] [Reference Citation Analysis]
110 Althenayan AS, Alsalamah SA, Aly S, Nouh T, Mirza AA. Detection and Classification of COVID-19 by Radiological Imaging Modalities Using Deep Learning Techniques: A Literature Review. Applied Sciences 2022;12:10535. [DOI: 10.3390/app122010535] [Reference Citation Analysis]
111 Bhattacharjya U, Sarma KK, Medhi JP, Choudhury BK, Barman G. Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database. Biomedical Signal Processing and Control 2022. [DOI: 10.1016/j.bspc.2022.104297] [Reference Citation Analysis]
112 Ayadi M, Ksibi A, Al-rasheed A, Soufiene BO. COVID-AleXception: A Deep Learning Model Based on a Deep Feature Concatenation Approach for the Detection of COVID-19 from Chest X-ray Images. Healthcare 2022;10:2072. [DOI: 10.3390/healthcare10102072] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
113 Hilty MP, Favaron E, Wendel Garcia PD, Ahiska Y, Uz Z, Akin S, Flick M, Arbous S, Hofmaenner DA, Saugel B, Endeman H, Schuepbach RA, Ince C. Microcirculatory alterations in critically ill COVID-19 patients analyzed using artificial intelligence. Crit Care 2022;26:311. [PMID: 36242010 DOI: 10.1186/s13054-022-04190-y] [Reference Citation Analysis]
114 Rahhal MMA, Bazi Y, Jomaa RM, Zuair M, Melgani F. Contrasting EfficientNet, ViT, and gMLP for COVID-19 Detection in Ultrasound Imagery. J Pers Med 2022;12:1707. [PMID: 36294846 DOI: 10.3390/jpm12101707] [Reference Citation Analysis]
115 Sinwar D, Dhaka VS, Tesfaye BA, Raghuwanshi G, Kumar A, Maakar SK, Agrawal S. Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey. Contrast Media Mol Imaging 2022;2022:1306664. [PMID: 36304775 DOI: 10.1155/2022/1306664] [Reference Citation Analysis]
116 Jiang L, Li M, Jiang H, Tao L, Yang W, Yuan H, He B. Development of an Artificial Intelligence Model for Analyzing the Relationship between Imaging Features and Glucocorticoid Sensitivity in Idiopathic Interstitial Pneumonia. Int J Environ Res Public Health 2022;19. [PMID: 36293674 DOI: 10.3390/ijerph192013099] [Reference Citation Analysis]
117 Bhandari M, Shahi TB, Siku B, Neupane A. Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI. Comput Biol Med 2022;150:106156. [PMID: 36228463 DOI: 10.1016/j.compbiomed.2022.106156] [Reference Citation Analysis]
118 Draelos RL, Carin L. Explainable multiple abnormality classification of chest CT volumes. Artificial Intelligence in Medicine 2022;132:102372. [DOI: 10.1016/j.artmed.2022.102372] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
119 Bayan Alsaaidah, Moh’d Rasoul Al-Hadidi, Heba Al-Nsour, Raja Masadeh, Nael AlZubi. Comprehensive Survey of Machine Learning Systems for COVID-19 Detection. J Imaging 2022;8:267. [PMID: 36286361 DOI: 10.3390/jimaging8100267] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
120 Sanghvi HA, Patel RH, Agarwal A, Gupta S, Sawhney V, Pandya AS. A deep learning approach for classification of COVID and pneumonia using DenseNet ‐201. Int J Imaging Syst Tech. [DOI: 10.1002/ima.22812] [Reference Citation Analysis]
121 Muzoğlu N, Halefoğlu AM, Avci MO, Kaya Karaaslan M, Yarman BSB. Detection of COVID ‐19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods. Expert Systems. [DOI: 10.1111/exsy.13141] [Reference Citation Analysis]
122 Liu XP, Yang X, Xiong M, Mao X, Jin X, Li Z, Zhou S, Chang H. Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening. Front Public Health 2022;10:1004117. [PMID: 36211676 DOI: 10.3389/fpubh.2022.1004117] [Reference Citation Analysis]
123 O' Doherty J, O' Doherty S, Abreu C, Aguiar A, Reilhac A, Robins E. Evolving operational guidance and experiences for radiology and nuclear medicine facilities in response to and beyond the COVID-19 pandemic. Br J Radiol 2022;95:20200511. [PMID: 35930772 DOI: 10.1259/bjr.20200511] [Reference Citation Analysis]
124 Chen J, Li Y, Guo L, Zhou X, Zhu Y, He Q, Han H, Feng Q. Machine learning techniques for CT imaging diagnosis of novel coronavirus pneumonia: a review. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07709-0] [Reference Citation Analysis]
125 Lu X, Xu Y, Yuan W. DBF-Net: a semi-supervised dual-task balanced fusion network for segmenting infected regions from lung CT images. Evolving Systems. [DOI: 10.1007/s12530-022-09466-w] [Reference Citation Analysis]
126 El Khediri S, Thaljaoui A, Alfayez F. A Novel Decision-Making Process for COVID-19 Fighting Based on Association Rules and Bayesian Methods. The Computer Journal 2022;65:2360-2376. [DOI: 10.1093/comjnl/bxab071] [Reference Citation Analysis]
127 Bhosale YH, Patnaik KS. Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review. Neural Process Lett. [DOI: 10.1007/s11063-022-11023-0] [Reference Citation Analysis]
128 Trușculescu AA, Manolescu DL, Broască L, Ancușa VM, Ciocârlie H, Pescaru CC, Vaștag E, Oancea CI. Enhancing Imagistic Interstitial Lung Disease Diagnosis by Using Complex Networks. Medicina 2022;58:1288. [DOI: 10.3390/medicina58091288] [Reference Citation Analysis]
129 Pellegrino F, Carnevale A, Bisi R, Cavedagna D, Reverberi R, Uccelli L, Leprotti S, Giganti M. Best Practices on Radiology Department Workflow: Tips from the Impact of the COVID-19 Lockdown on an Italian University Hospital. Healthcare (Basel) 2022;10. [PMID: 36141383 DOI: 10.3390/healthcare10091771] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
130 Abirami N, Vincent DR, Kadry S. P2P-COVID-GAN. Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention 2022. [DOI: 10.4018/978-1-6684-7544-7.ch037] [Reference Citation Analysis]
131 Ambrosetti M, Battocchio G, Montemezzi S, Cattazzo F, Bejko T, Tacconelli E, Minuz P, Crisafulli E, Fava C, Mansueto G. The Caliber of Segmental and Subsegmental Vessels in COVID-19 Pneumonia Is Enlarged: A Distinctive Feature in Comparison with Other Forms of Inflammatory and Thromboembolic Diseases. JPM 2022;12:1465. [DOI: 10.3390/jpm12091465] [Reference Citation Analysis]
132 Roth HR, Xu Z, Tor-Díez C, Sanchez Jacob R, Zember J, Molto J, Li W, Xu S, Turkbey B, Turkbey E, Yang D, Harouni A, Rieke N, Hu S, Isensee F, Tang C, Yu Q, Sölter J, Zheng T, Liauchuk V, Zhou Z, Moltz JH, Oliveira B, Xia Y, Maier-Hein KH, Li Q, Husch A, Zhang L, Kovalev V, Kang L, Hering A, Vilaça JL, Flores M, Xu D, Wood B, Linguraru MG. Rapid artificial intelligence solutions in a pandemic-The COVID-19-20 Lung CT Lesion Segmentation Challenge. Med Image Anal 2022;82:102605. [PMID: 36156419 DOI: 10.1016/j.media.2022.102605] [Reference Citation Analysis]
133 Tiwari A, Tripathi S, Pandey DC, Sharma N, Sharma S. Detection of COVID-19 Infection in CT and X-ray images using transfer learning approach. Technol Health Care 2022. [PMID: 36093719 DOI: 10.3233/THC-220114] [Reference Citation Analysis]
134 Chandra TB, Singh BK, Jain D. Integrating patient symptoms, clinical readings, and radiologist feedback with computer-aided diagnosis system for detection of infectious pulmonary disease: a feasibility study. Med Biol Eng Comput 2022;60:2549-2565. [DOI: 10.1007/s11517-022-02611-2] [Reference Citation Analysis]
135 Shang Y, Wei Z, Hui H, Li X, Li L, Yu Y, Lu L, Li L, Li H, Yang Q, Wang M, Zhan M, Wang W, Zhang G, Wu X, Wang L, Liu J, Tian J, Zha Y. Two-stage hybrid network for segmentation of COVID-19 pneumonia lesions in CT images: a multicenter study. Med Biol Eng Comput 2022;60:2721-2736. [DOI: 10.1007/s11517-022-02619-8] [Reference Citation Analysis]
136 Zhang X, Jiang R, Huang P, Wang T, Hu M, Scarsbrook AF, Frangi AF. Dynamic feature learning for COVID-19 segmentation and classification. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.106136] [Reference Citation Analysis]
137 Ascencio-cabral A, Reyes-aldasoro CC. Comparison of Convolutional Neural Networks and Transformers for the Classification of Images of COVID-19, Pneumonia and Healthy Individuals as Observed with Computed Tomography. J Imaging 2022;8:237. [DOI: 10.3390/jimaging8090237] [Reference Citation Analysis]
138 Wick KD, Aggarwal NR, Curley MAQ, Fowler AA 3rd, Jaber S, Kostrubiec M, Lassau N, Laterre PF, Lebreton G, Levitt JE, Mebazaa A, Rubin E, Sinha P, Ware LB, Matthay MA. Opportunities for improved clinical trial designs in acute respiratory distress syndrome. Lancet Respir Med 2022;10:916-24. [PMID: 36057279 DOI: 10.1016/S2213-2600(22)00294-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
139 Özdemir Ö, Sönmez EB. Attention mechanism and mixup data augmentation for classification of COVID-19 Computed Tomography images. Journal of King Saud University - Computer and Information Sciences 2022;34:6199-6207. [DOI: 10.1016/j.jksuci.2021.07.005] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
140 Yi J, Zhang H, Mao J, Chen Y, Zhong H, Wang Y. Review on the COVID-19 pandemic prevention and control system based on AI. Eng Appl Artif Intell 2022;114:105184. [PMID: 35846728 DOI: 10.1016/j.engappai.2022.105184] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
141 Palmisano A, Vignale D, Boccia E, Nonis A, Gnasso C, Leone R, Montagna M, Nicoletti V, Bianchi AG, Brusamolino S, Dorizza A, Moraschini M, Veettil R, Cereda A, Toselli M, Giannini F, Loffi M, Patelli G, Monello A, Iannopollo G, Ippolito D, Mancini EM, Pontone G, Vignali L, Scarnecchia E, Iannacone M, Baffoni L, Sperandio M, de Carlini CC, Sironi S, Rapezzi C, Antiga L, Jagher V, Di Serio C, Furlanello C, Tacchetti C, Esposito A. AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients. Radiol med 2022;127:960-972. [DOI: 10.1007/s11547-022-01518-0] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
142 Smadi AA, Abugabah A, Al-Smadi AM, Almotairi S. SEL-COVIDNET: An intelligent application for the diagnosis of COVID-19 from chest X-rays and CT-scans. Inform Med Unlocked 2022;:101059. [PMID: 36033909 DOI: 10.1016/j.imu.2022.101059] [Reference Citation Analysis]
143 Sun W, Chen J, Yan L, Lin J, Pang Y, Zhang G. COVID-19 CT image segmentation method based on swin transformer. Front Physiol 2022;13:981463. [DOI: 10.3389/fphys.2022.981463] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
144 Perepi R, K S, Chattopadhyay P, O AB. A deep learning computational approach for the classification of COVID-19 virus. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2022. [DOI: 10.1080/21681163.2022.2111722] [Reference Citation Analysis]
145 Montaha S, Azam S, Rafid AKMRH, Hasan MZ, Karim A, Hasib KM, Patel SK, Jonkman M, Mannan ZI. MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique. Front Med 2022;9. [DOI: 10.3389/fmed.2022.924979] [Reference Citation Analysis]
146 Qayyum A, Lalande A, Meriaudeau F. Effective multiscale deep learning model for COVID19 segmentation tasks: A further step towards helping radiologist. Neurocomputing 2022;499:63-80. [PMID: 35578654 DOI: 10.1016/j.neucom.2022.05.009] [Reference Citation Analysis]
147 Alrahhal M, K P S. COVID-19 Diagnostic System Using Medical Image Classification and Retrieval: A Novel Method for Image Analysis. The Computer Journal 2022;65:2146-2163. [DOI: 10.1093/comjnl/bxab051] [Reference Citation Analysis]
148 Sajid MI, Ahmed S, Waqar U, Tariq J, Chundrigarh M, Balouch SS, Abaidullah S. SARS-CoV-2: Has artificial intelligence stood the test of time. Chin Med J (Engl) 2022;135:1792-802. [PMID: 36195992 DOI: 10.1097/CM9.0000000000002058] [Reference Citation Analysis]
149 Agrawal S, Singh A, Tiwari A, Mishra A, Tripathi A. A Systematic Survey on COVID 19 Detection and Diagnosis by Utilizing Deep Learning Techniques and Modalities of Radiology. Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing 2022. [DOI: 10.1145/3549206.3549283] [Reference Citation Analysis]
150 Jain DK, Singh T, Saurabh P, Bisen D, Sahu N, Mishra J, Rahman H, Sharma K. Deep Learning-Aided Automated Pneumonia Detection and Classification Using CXR Scans. Computational Intelligence and Neuroscience 2022;2022:1-19. [DOI: 10.1155/2022/7474304] [Reference Citation Analysis]
151 Albahli S, Meraj T, Chakraborty C, Rauf HT. AI-driven deep and handcrafted features selection approach for Covid-19 and chest related diseases identification. Multimed Tools Appl. [DOI: 10.1007/s11042-022-13499-3] [Reference Citation Analysis]
152 Perez-careta E, Hernández-farías DI, Guzman-sepulveda JR, Cisneros MT, Cordoba-fraga T, Martinez Espinoza JC, Guzman-cabrera R. One-class Classification for Identifying COVID-19 in X-Ray Images. Program Comput Soft 2022;48:235-242. [DOI: 10.1134/s0361768822040041] [Reference Citation Analysis]
153 Li X, Zhao H, Ren T, Tian Y, Yan A, Li W. Inverted papilloma and nasal polyp classification using a deep convolutional network integrated with an attention mechanism. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.105976] [Reference Citation Analysis]
154 Zhang H, Liang W, Li C, Xiong Q, Shi H, Hu L, Li G. DCML: Deep contrastive mutual learning for COVID-19 recognition. Biomedical Signal Processing and Control 2022;77:103770. [DOI: 10.1016/j.bspc.2022.103770] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
155 Sedaghati N, Abbasi B. Study of Metadata Impact on COVID-19 Detection using Convolutional Neural Networks. 2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI) 2022. [DOI: 10.1109/iri54793.2022.00070] [Reference Citation Analysis]
156 Do TD, Skornitzke S, Merle U, Kittel M, Hofbaur S, Melzig C, Kauczor H, Wielpütz MO, Weinheimer O. COVID-19 pneumonia: Prediction of patient outcome by CT-based quantitative lung parenchyma analysis combined with laboratory parameters. PLoS ONE 2022;17:e0271787. [DOI: 10.1371/journal.pone.0271787] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
157 Asif S, Wenhui Y, Amjad K, Jin H, Tao Y, Jinhai S. Detection of COVID ‐19 from chest X‐ray images: Boosting the performance with convolutional neural network and transfer learning. Expert Systems. [DOI: 10.1111/exsy.13099] [Reference Citation Analysis]
158 Sajid MI, Ahmed S, Waqar U, Tariq J, Chundrigarh M, Balouch SS, Abaidullah S. Application in medicine: Has artificial intelligence stood the test of time. Chin Med J (Engl) 2022. [PMID: 35899989 DOI: 10.1097/CM9.00000000000020S8] [Reference Citation Analysis]
159 Broască L, Trușculescu AA, Ancușa VM, Ciocârlie H, Oancea CI, Stoicescu ER, Manolescu DL. A Novel Method for Lung Image Processing Using Complex Networks. Tomography 2022;8:1928-46. [PMID: 35894027 DOI: 10.3390/tomography8040162] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
160 Rajamani KT, Rani P, Siebert H, ElagiriRamalingam R, Heinrich MP. Attention-augmented U-Net (AA-U-Net) for semantic segmentation. Signal Image Video Process 2022;:1-9. [PMID: 35910403 DOI: 10.1007/s11760-022-02302-3] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
161 Eyiokur FI, Ekenel HK, Waibel A. Unconstrained face mask and face-hand interaction datasets: building a computer vision system to help prevent the transmission of COVID-19. SIViP. [DOI: 10.1007/s11760-022-02308-x] [Reference Citation Analysis]
162 Mr. Sharan L. Pais, Rakshitha R, Rashmi S K, Ravish, Sathwik U Shetty. Robotics and Artificial Intelligence in Healthcare During COVID-19 Pandemic. IJARSCT 2022. [DOI: 10.48175/ijarsct-5831] [Reference Citation Analysis]
163 Islam T, Absar S, Ali Ijtihad Nasif SM, Sakib Mridul S. Deep Neural Network models for diagnosis of COVID-19 Respiratory diseases by analyzing CT-Scans and Explain-ability using trained models. 2022 International Conference on Inventive Computation Technologies (ICICT) 2022. [DOI: 10.1109/icict54344.2022.9850458] [Reference Citation Analysis]
164 Xu Z, Guo K, Chu W, Lou J, Chen C. Performance of Machine Learning Algorithms for Predicting Adverse Outcomes in Community-Acquired Pneumonia. Front Bioeng Biotechnol 2022;10:903426. [PMID: 35845426 DOI: 10.3389/fbioe.2022.903426] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
165 Priya GK, Rani DGN. VLSI Implementation of Optimized CNN based COVID-19 Lung Infection Segmentation and Classification from CT Image. 2022 IEEE India Council International Subsections Conference (INDISCON) 2022. [DOI: 10.1109/indiscon54605.2022.9862864] [Reference Citation Analysis]
166 Dalal S, Vishwakarma VP, Sisaudia V, Narwal P. Non-iterative learning machine for identifying CoViD19 using chest X-ray images. Sci Rep 2022;12:11880. [PMID: 35831332 DOI: 10.1038/s41598-022-15268-6] [Reference Citation Analysis]
167 Dinar AM, Raheem EA, Abdulkareem KH, Mohammed MA, Oleiwie MG, Zayr FH, Al-boridi O, Al-mhiqani MN, Al-andoli MN, Khattak HA. Towards Automated Multiclass Severity Prediction Approach for COVID-19 Infections Based on Combinations of Clinical Data. Mobile Information Systems 2022;2022:1-8. [DOI: 10.1155/2022/7675925] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
168 Yıldırım Ayaz E, Coşkun ZÜ, Kaplan M, Bulut AS, Yeşildal M, Ankaralı H, Uygun G, Telci Çaklılı Ö, Uzunlulu M, Vahaboğlu H, Odabaş AR. Comparison of Initial CT Findings and CO-RADS Stage in COVID-19 Patients with PCR, Inflammation and Coagulation Parameters in Diagnostic and Prognostic Perspectives. Journal of the Belgian Society of Radiology 2022;106. [DOI: 10.5334/jbsr.2714] [Reference Citation Analysis]
169 Rasheed J. Analyzing the Effect of Filtering and Feature-Extraction Techniques in a Machine Learning Model for Identification of Infectious Disease Using Radiography Imaging. Symmetry 2022;14:1398. [DOI: 10.3390/sym14071398] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
170 Rithesh K, Wong L, See J, Chan W, Ng K. DECOVID-CT: Lightweight 3D CNN for COVID-19 Infection Prediction. 2022 IEEE International Conference on Consumer Electronics - Taiwan 2022. [DOI: 10.1109/icce-taiwan55306.2022.9869238] [Reference Citation Analysis]
171 Mugdadi E, Hmeidi I, Al-aiad A, Obeidat N. Deep learning approach for classifying CT images of COVID-19: A Systematic Review. 2022 International Conference on Engineering & MIS (ICEMIS) 2022. [DOI: 10.1109/icemis56295.2022.9914004] [Reference Citation Analysis]
172 Mohamed Akram K, Sihem S, Okba K, Harous S. IoMT-fog-cloud based architecture for Covid-19 detection. Biomedical Signal Processing and Control 2022;76:103715. [DOI: 10.1016/j.bspc.2022.103715] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
173 Dasgupta A, Bakshi A, Mukherjee S, Das K, Talukdar S, Chatterjee P, Mondal S, Das P, Ghosh S, Som A, Roy P, Kundu R, Sarkar A, Biswas A, Paul K, Basak S, Manna K, Saha C, Mukhopadhyay S, Bhattacharyya NP, De RK. Epidemiological challenges in pandemic coronavirus disease ( COVID ‐19): Role of artificial intelligence. WIREs Data Min & Knowl 2022;12. [DOI: 10.1002/widm.1462] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
174 Filchakova O, Dossym D, Ilyas A, Kuanysheva T, Abdizhamil A, Bukasov R. Review of COVID-19 testing and diagnostic methods. Talanta 2022;244:123409. [PMID: 35390680 DOI: 10.1016/j.talanta.2022.123409] [Cited by in Crossref: 30] [Cited by in F6Publishing: 21] [Article Influence: 30.0] [Reference Citation Analysis]
175 Zeng Z, Fan C, Xiao L, Qu X. DEA-UNet: a dense-edge-attention UNet architecture for medical image segmentation. J Electron Imag 2022;31. [DOI: 10.1117/1.jei.31.4.043032] [Reference Citation Analysis]
176 Sharma G, Umapathy K, Krishnan S. Audio texture analysis of COVID-19 cough, breath, and speech sounds. Biomedical Signal Processing and Control 2022;76:103703. [DOI: 10.1016/j.bspc.2022.103703] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
177 Sadik F, Dastider AG, Subah MR, Mahmud T, Fattah SA. A dual-stage deep convolutional neural network for automatic diagnosis of COVID-19 and pneumonia from chest CT images. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.105806] [Reference Citation Analysis]
178 Gaonkar A, Gupta P, Sawant D. Technology in a fight against COVID-19. 2022 IEEE Region 10 Symposium (TENSYMP) 2022. [DOI: 10.1109/tensymp54529.2022.9864558] [Reference Citation Analysis]
179 Yaşar H, Ceylan M, Cebeci H, Kılınçer A, Kanat F, Koplay M. A novel study to increase the classification parameters on automatic three-class COVID-19 classification from CT images, including cases from Turkey. Journal of Experimental & Theoretical Artificial Intelligence. [DOI: 10.1080/0952813x.2022.2093980] [Reference Citation Analysis]
180 Khan RN, Hussain L, Alluhaidan AS, Majid A, Lone KJ, Verdiyev R, Al-wesabi FN, Duong TQ. COVID-19 lung infection detection using deep learning with transfer learning and ResNet101 features extraction and selection. Waves in Random and Complex Media. [DOI: 10.1080/17455030.2022.2091807] [Reference Citation Analysis]
181 Eappen P. Healthcare Informatics During the COVID-19 Pandemic. Advances in Logistics, Operations, and Management Science 2022. [DOI: 10.4018/978-1-6684-5279-0.ch010] [Reference Citation Analysis]
182 Furtado A, da Purificação CAC, Badaró R, Nascimento EGS. A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia. Diagnostics 2022;12:1527. [DOI: 10.3390/diagnostics12071527] [Reference Citation Analysis]
183 Thurzo A, Jančovičová V, Hain M, Thurzo M, Novák B, Kosnáčová H, Lehotská V, Varga I, Kováč P, Moravanský N. Human Remains Identification Using Micro-CT, Chemometric and AI Methods in Forensic Experimental Reconstruction of Dental Patterns after Concentrated Sulphuric Acid Significant Impact. Molecules 2022;27:4035. [DOI: 10.3390/molecules27134035] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
184 Gong H, Wang M, Zhang H, Elahe MF, Jin M. An Explainable AI Approach for the Rapid Diagnosis of COVID-19 Using Ensemble Learning Algorithms. Front Public Health 2022;10:874455. [DOI: 10.3389/fpubh.2022.874455] [Reference Citation Analysis]
185 Risoli C, Nicolò M, Colombi D, Moia M, Rapacioli F, Anselmi P, Michieletti E, Ambrosini R, Di Terlizzi M, Grazioli L, Colmo C, Di Naro A, Natale MP, Tombolesi A, Adraman A, Tuttolomondo D, Costantino C, Vetti E, Martini C. Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients. Diagnostics 2022;12:1501. [DOI: 10.3390/diagnostics12061501] [Reference Citation Analysis]
186 Tello-mijares S, Woo F. Novel COVID-19 Diagnosis Delivery App Using Computed Tomography Images Analyzed with Saliency-Preprocessing and Deep Learning. Tomography 2022;8:1618-1630. [DOI: 10.3390/tomography8030134] [Reference Citation Analysis]
187 Durga R, Poovammal E. FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction. Front Public Health 2022;10:892499. [DOI: 10.3389/fpubh.2022.892499] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
188 Mahanty C, Kumar R, Patro SGK. Internet of Medical Things-Based COVID-19 Detection in CT Images Fused with Fuzzy Ensemble and Transfer Learning Models. New Gener Comput 2022;:1-17. [PMID: 35730008 DOI: 10.1007/s00354-022-00176-0] [Reference Citation Analysis]
189 Wang T, Zhang Y, Liu C, Zhou Z. Artificial intelligence against the first wave of COVID-19: evidence from China. BMC Health Serv Res 2022;22:767. [PMID: 35689275 DOI: 10.1186/s12913-022-08146-4] [Reference Citation Analysis]
190 Mrs. S. Farjana Farvin, Dinesh Kumar. R. P, Gothandaraman. A. A Novel Approach for Automatic Detection of the Coronavirus Disease from CT Images Using an Optimized Convolutional Neural Network. IJARSCT 2022. [DOI: 10.48175/ijarsct-4607] [Reference Citation Analysis]
191 Saeed RR, Yaseen OM, Rashid MM, Ahmed MR. Applications of Machine Learning in Battling Against Novel COVID-19. 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) 2022. [DOI: 10.1109/hora55278.2022.9799969] [Reference Citation Analysis]
192 Dandil E, Yildirim MS. Automatic Segmentation of COVID-19 Infection on Lung CT Scans using Mask R-CNN. 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) 2022. [DOI: 10.1109/hora55278.2022.9800029] [Reference Citation Analysis]
193 Bermejo-peláez D, San José Estépar R, Fernández-velilla M, Palacios Miras C, Gallardo Madueño G, Benegas M, Gotera Rivera C, Cuerpo S, Luengo-oroz M, Sellarés J, Sánchez M, Bastarrika G, Peces Barba G, Seijo LM, Ledesma-carbayo MJ. Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT. Sci Rep 2022;12. [DOI: 10.1038/s41598-022-13298-8] [Reference Citation Analysis]
194 Khan Z, Umar AI, Shirazi SH, Rasheed A, Yousaf W, Assam M, Hassan I, Mohamed A. Lung’s Segmentation Using Context-Aware Regressive Conditional GAN. Applied Sciences 2022;12:5768. [DOI: 10.3390/app12125768] [Reference Citation Analysis]
195 Bai Y, Arif M. Strategies for Improving the Quality of Music Teaching in Primary and Secondary Schools in the Context of Artificial Intelligence and Evaluation. Security and Communication Networks 2022;2022:1-7. [DOI: 10.1155/2022/4680905] [Reference Citation Analysis]
196 Zadeh FA, Ardalani MV, Salehi AR, Jalali Farahani R, Hashemi M, Mohammed AH. An Analysis of New Feature Extraction Methods Based on Machine Learning Methods for Classification Radiological Images. Comput Intell Neurosci 2022;2022:3035426. [PMID: 35634075 DOI: 10.1155/2022/3035426] [Reference Citation Analysis]
197 Taran Rishit UNDRU, Utkarsha UDAY, Jyothi Tadi LAKSHMI, Ariyanachi KALIAPPAN, Saranya MALLAMGUNTA, Shalam Sheerin NIKHAT, V SAKTHIVADIVEL, Archana GAUR. Integrating Artificial Intelligence for Clinical and Laboratory Diagnosis – a Review. Maedica (Bucur) 2022;17. [PMID: 36032592 DOI: 10.26574/maedica.2022.17.2.420] [Reference Citation Analysis]
198 Fallahpoor M, Chakraborty S, Heshejin MT, Chegeni H, Horry MJ, Pradhan B. Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection. Comput Biol Med 2022;145:105464. [PMID: 35390746 DOI: 10.1016/j.compbiomed.2022.105464] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
199 Gupta PK, Siddiqui MK, Huang X, Morales-Menendez R, Pawar H, Terashima-Marin H, Wajid MS. COVID-WideNet-A capsule network for COVID-19 detection. Appl Soft Comput 2022;122:108780. [PMID: 35369122 DOI: 10.1016/j.asoc.2022.108780] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
200 Ramírez Varela A, Moreno López S, Contreras-arrieta S, Tamayo-cabeza G, Restrepo-restrepo S, Sarmiento-barbieri I, Caballero-díaz Y, Jorge Hernandez-florez L, Mario González J, Salas-zapata L, Laajaj R, Buitrago-gutierrez G, de la Hoz-restrepo F, Vives Florez M, Osorio E, Sofía Ríos-oliveros D, Behrentz E. Prediction of SARS-CoV-2 infection with a Symptoms-Based model to aid public health decision making in Latin America and other low and middle income settings. Preventive Medicine Reports 2022;27:101798. [DOI: 10.1016/j.pmedr.2022.101798] [Reference Citation Analysis]
201 Fahad SD, Gharghan SK, Hussein RH. DIAGNOSIS OF COVID-19 BASED ON ARTIFICIAL INTELLIGENCE MODELS AND PHYSIOLOGICAL SENSORS: REVIEW. Biomed Eng Appl Basis Commun 2022;34. [DOI: 10.4015/s1016237222500065] [Reference Citation Analysis]
202 Ghashghaei S, Wood DA, Sadatshojaei E, Jalilpoor M. Grayscale image statistics of COVID‐19 patient CT scans characterize lung condition with machine and deep learning. Chronic Diseases and Translational Medicine. [DOI: 10.1002/cdt3.27] [Reference Citation Analysis]
203 Sri Kavya N, Shilpa T, Veeranjaneyulu N, Divya Priya D. Detecting Covid19 and pneumonia from chest X-ray images using deep convolutional neural networks. Mater Today Proc 2022;64:737-43. [PMID: 35607444 DOI: 10.1016/j.matpr.2022.05.199] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
204 Ostrovskyi M, Konopkina L, Gashynova K, Gumeniuk G, Dobrianskyi D, Bororova O. Pathogenetic treatment of patients with COVID-19 at the outpatient stage. IC 2022. [DOI: 10.32902/2663-0338-2022-1-23-31] [Reference Citation Analysis]
205 Anand S, Nishant N, Singal T. Automated detection of COVID-19 from chest X-rays using CNN. 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS) 2022. [DOI: 10.1109/iciccs53718.2022.9788336] [Reference Citation Analysis]
206 Chouat I, Echtioui A, Khemakhem R, Zouch W, Ghorbel M, Hamida AB. Lung Disease Detection in Chest X-ray Images Using Transfer Learning. 2022 6th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) 2022. [DOI: 10.1109/atsip55956.2022.9805892] [Reference Citation Analysis]
207 Tobón DP, Hossain MS, Muhammad G, Bilbao J, Saddik AE. Deep learning in multimedia healthcare applications: a review. Multimedia Systems. [DOI: 10.1007/s00530-022-00948-0] [Reference Citation Analysis]
208 Swanson T, Zelner J, Guikema S. COVID-19 has illuminated the need for clearer AI-based risk management strategies. Journal of Risk Research. [DOI: 10.1080/13669877.2022.2077411] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
209 Chicaiza J, Villota SD, Vinueza-Naranjo PG, Rumipamba-Zambrano R. Contribution of Deep-Learning Techniques Toward Fighting COVID-19: A Bibliometric Analysis of Scholarly Production During 2020. IEEE Access 2022;10:33281-300. [PMID: 35582497 DOI: 10.1109/ACCESS.2022.3159025] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
210 Meraihi Y, Gabis AB, Mirjalili S, Ramdane-Cherif A, Alsaadi FE. Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey. SN Comput Sci 2022;3:286. [PMID: 35578678 DOI: 10.1007/s42979-022-01184-z] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
211 Laudanski K, Scott M, Huffenberger AM, Wain J, Hanson CW. Deployment of Tele-ICU Respiratory Therapy and the Creation of an eRT Service Line. NEJM Catalyst 2022;3. [DOI: 10.1056/cat.21.0239] [Reference Citation Analysis]
212 Mrs. Komal Katore, Prof. Sachin Thanekar. A Noise-Resilient Framework for Automatic COVID-19 Pneumonia Lesions Segmentation from CT Images. IJARSCT 2022. [DOI: 10.48175/ijarsct-3746] [Reference Citation Analysis]
213 Ferjaoui R, Cherni MA, Abidi F, Zidi A. Deep Residual Learning based on ResNet50 for COVID-19 Recognition in Lung CT Images. 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT) 2022. [DOI: 10.1109/codit55151.2022.9804094] [Reference Citation Analysis]
214 Karim AM, Kaya H, Alcan V, Sen B, Hadimlioglu IA. New Optimized Deep Learning Application for COVID-19 Detection in Chest X-ray Images. Symmetry 2022;14:1003. [DOI: 10.3390/sym14051003] [Reference Citation Analysis]
215 Albalawi U, Mustafa M. Current Artificial Intelligence (AI) Techniques, Challenges, and Approaches in Controlling and Fighting COVID-19: A Review. Int J Environ Res Public Health 2022;19:5901. [PMID: 35627437 DOI: 10.3390/ijerph19105901] [Reference Citation Analysis]
216 Saqib M, Anwar A, Anwar S, Petersson L, Sharma N, Blumenstein M. COVID-19 Detection from Radiographs: Is Deep Learning Able to Handle the Crisis? Signals 2022;3:296-312. [DOI: 10.3390/signals3020019] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
217 Mohebbi N, Tutunchian M, Alavi M, Kargari M, Kharazmy AB. Supervised Machine Learning Models for Covid-19 Diagnosis using a Combination of Clinical and Laboratory Data. 2022 8th International Conference on Web Research (ICWR) 2022. [DOI: 10.1109/icwr54782.2022.9786248] [Reference Citation Analysis]
218 [DOI: 10.1109/icibt52874.2022.9807725] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
219 Gu M, Pan H, Yuan Y, Zhou X, Chen L, Wang X, Fang F, Hu L, Xie Y, Shen C. Sera Metabolomics Characterization of Patients at Different Stages in Wuhan Identifies Critical Biomarkers of COVID-19. Front Cell Infect Microbiol 2022;12:882661. [DOI: 10.3389/fcimb.2022.882661] [Reference Citation Analysis]
220 Zhao W, Zhang J, Liu X, Jiang Z. Application of ISO 26000 in digital education during COVID-19. Ain Shams Engineering Journal 2022;13:101630. [DOI: 10.1016/j.asej.2021.10.025] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
221 Li CF, Xu YD, Ding XH, Zhao JJ, Du RQ, Wu LZ, Sun WP. MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification. Comput Biol Med 2022;144:105340. [PMID: 35305504 DOI: 10.1016/j.compbiomed.2022.105340] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
222 Guo S, Wang G, Han L, Song X, Yang W. COVID-19 CT image denoising algorithm based on adaptive threshold and optimized weighted median filter. Biomedical Signal Processing and Control 2022;75:103552. [DOI: 10.1016/j.bspc.2022.103552] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
223 Pellikka PA. Artificially Intelligent Interpretation of Stress Echocardiography: The Future Is Now. JACC Cardiovasc Imaging 2022;15:728-30. [PMID: 35512949 DOI: 10.1016/j.jcmg.2021.11.010] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
224 Aggarwal P, Mishra NK, Fatimah B, Singh P, Gupta A, Joshi SD. COVID-19 image classification using deep learning: Advances, challenges and opportunities. Comput Biol Med 2022;144:105350. [PMID: 35305501 DOI: 10.1016/j.compbiomed.2022.105350] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 12.0] [Reference Citation Analysis]
225 Zhang M, Ding C, Guo S. Analysis of Tracheobronchial Diverticula Based on Semantic Segmentation of CT Images via the Dual-Channel Attention Network. Front Public Health 2021;9:813717. [PMID: 35071176 DOI: 10.3389/fpubh.2021.813717] [Reference Citation Analysis]
226 Hamdi S, Oussalah M, Moussaoui A, Saidi M. Attention-based hybrid CNN-LSTM and spectral data augmentation for COVID-19 diagnosis from cough sound. J Intell Inf Syst 2022;:1-23. [PMID: 35498369 DOI: 10.1007/s10844-022-00707-7] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
227 Saleem F, Al-Ghamdi ASA, Alassafi MO, AlGhamdi SA. Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review. Int J Environ Res Public Health 2022;19:5099. [PMID: 35564493 DOI: 10.3390/ijerph19095099] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
228 Wilder-Smith AJ, Yang S, Weikert T, Bremerich J, Haaf P, Segeroth M, Ebert LC, Sauter A, Sexauer R. Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network. Diagnostics (Basel) 2022;12. [PMID: 35626201 DOI: 10.3390/diagnostics12051045] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
229 Fang X, Li W, Li W, Han Y, Huang J, Feng Q, Zhang J. Ultrasound image intelligent diagnosis in community-acquired pneumonia of children using convolutional neural network based transfer learning (Preprint).. [DOI: 10.2196/preprints.38810] [Reference Citation Analysis]
230 Attaullah M, Ali M, Almufareh MF, Ahmad M, Hussain L, Jhanjhi N, Humayun M. Initial Stage COVID-19 Detection System Based on Patients’ Symptoms and Chest X-Ray Images. Applied Artificial Intelligence. [DOI: 10.1080/08839514.2022.2055398] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
231 Masood MZ, Jamil A, Hameed AA. Efficient Artificial Intelligence-based Models for COVID-19 Disease Detection and Diagnosis from CT-Scans. 2022 2nd International Conference on Computing and Machine Intelligence (ICMI) 2022. [DOI: 10.1109/icmi55296.2022.9873659] [Reference Citation Analysis]
232 Liu F, Chen D, Zhou X, Dai W, Xu F. Let AI Perform Better Next Time—A Systematic Review of Medical Imaging-Based Automated Diagnosis of COVID-19: 2020–2022. Applied Sciences 2022;12:3895. [DOI: 10.3390/app12083895] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
233 Zheng B, Zhu Y, Shi Q, Yang D, Shao Y, Xu T. MA-Net:Mutex attention network for COVID-19 diagnosis on CT images. Appl Intell. [DOI: 10.1007/s10489-022-03431-5] [Reference Citation Analysis]
234 Aiello M, Baldi D, Esposito G, Valentino M, Randon M, Salvatore M, Cavaliere C. Evaluation of AI-Based Segmentation Tools for COVID-19 Lung Lesions on Conventional and Ultra-low Dose CT Scans. Dose-Response 2022;20:155932582210828. [DOI: 10.1177/15593258221082896] [Reference Citation Analysis]
235 Bermejo-pelaez D, San José Estépar R, Fernández-velilla M, Palacios Miras C, Gallardo Madueño G, Benegas M, Luengo Oroz M, Sellares J, Sánchez M, Bastarrika G, Peces-barba G, Seijo LM, Ledesma Carbayo MJ. Deep-learning characterization and quantification of COVID-19 pneumonia lesions from chest CT images. Medical Imaging 2022: Computer-Aided Diagnosis 2022. [DOI: 10.1117/12.2613086] [Reference Citation Analysis]
236 Aziz S, Ilyas QM, Mehmood A, Ahmad A. Role of Machine Learning in Handling the COVID-19 Pandemic. Empowering Sustainable Industrial 4.0 Systems With Machine Intelligence 2022. [DOI: 10.4018/978-1-7998-9201-4.ch011] [Reference Citation Analysis]
237 Nagaraj Y, de Jonge G, Andreychenko A, Presti G, Fink MA, Pavlov N, Quattrocchi CC, Morozov S, Veldhuis R, Oudkerk M, van Ooijen PMA. Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting. Eur Radiol 2022. [PMID: 35362751 DOI: 10.1007/s00330-022-08730-6] [Reference Citation Analysis]
238 Hotta K. Further Progress in Image Recognition Based on Deep Learning: with Focus on Unsupervised Representation Learning and Transformer. IEICE Fundamentals Review 2022;15:258-267. [DOI: 10.1587/essfr.15.4_258] [Reference Citation Analysis]
239 Muralidharan N, Gupta S, Prusty MR, Tripathy RK. Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network. Applied Soft Computing 2022;119:108610. [DOI: 10.1016/j.asoc.2022.108610] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
240 Verma A, Amin SB, Naeem M, Saha M. Detecting COVID-19 from chest computed tomography scans using AI-driven android application. Comput Biol Med 2022;143:105298. [PMID: 35220076 DOI: 10.1016/j.compbiomed.2022.105298] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 7.0] [Reference Citation Analysis]
241 Ebrahimian S, Kalra MK, Agarwal S, Bizzo BC, Elkholy M, Wald C, Allen B, Dreyer KJ. FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies. Acad Radiol 2022;29:559-66. [PMID: 34969610 DOI: 10.1016/j.acra.2021.09.002] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 12.0] [Reference Citation Analysis]
242 Zhu L, Zhang L, Hu W, Chen H, Li H, Wei S, Chen X, Ma X. A multi-task two-path deep learning system for predicting the invasiveness of craniopharyngioma. Computer Methods and Programs in Biomedicine 2022;216:106651. [DOI: 10.1016/j.cmpb.2022.106651] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
243 Wang S, Zhang X, Zhang Y. DSSAE: Deep Stacked Sparse Autoencoder Analytical Model for COVID-19 Diagnosis by Fractional Fourier Entropy. ACM Trans Manage Inf Syst 2022;13:1-20. [DOI: 10.1145/3451357] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 8.0] [Reference Citation Analysis]
244 Abdulkareem KH, Mostafa SA, Al-qudsy ZN, Mohammed MA, Al-waisy AS, Kadry S, Lee J, Nam Y, Chen M. Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models. Journal of Healthcare Engineering 2022;2022:1-13. [DOI: 10.1155/2022/5329014] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
245 Ortiz S, Rojas F, Valenzuela O, Herrera LJ, Rojas I. Determination of the Severity and Percentage of COVID-19 Infection through a Hierarchical Deep Learning System. JPM 2022;12:535. [DOI: 10.3390/jpm12040535] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
246 Yan S, Zhang H, Wang J. Trends and hot topics in radiology, nuclear medicine and medical imaging from 2011-2021: a bibliometric analysis of highly cited papers. Jpn J Radiol 2022. [PMID: 35344133 DOI: 10.1007/s11604-022-01268-z] [Reference Citation Analysis]
247 Li S, Liu J, Zhou Z, Zhou Z, Wu X, Li Y, Wang S, Liao W, Ying S, Zhao Z. Artificial intelligence for caries and periapical periodontitis detection. J Dent 2022;:104107. [PMID: 35341892 DOI: 10.1016/j.jdent.2022.104107] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 9.0] [Reference Citation Analysis]
248 Karayel T, Kurutkan MN. BIBLIOMETRIC ANALYSIS OF PUBLICATIONS RELATED TO ARTIFICIAL INTELLIGENCE AND ITS COMPONENTS IN THE COVID-19 PERIOD. SagAkaDerg 2022. [DOI: 10.52880/sagakaderg.1070774] [Reference Citation Analysis]
249 Pandey D, Pandey K. An Extended Deep Learning based Solution for Screening COVID-19 CT-Scans. 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom) 2022. [DOI: 10.23919/indiacom54597.2022.9763194] [Reference Citation Analysis]
250 Das D, Biswas SK, Bandyopadhyay S. Perspective of AI system for COVID-19 detection using chest images: a review. Multimed Tools Appl 2022;81:21471-501. [PMID: 35310889 DOI: 10.1007/s11042-022-11913-4] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
251 Cilloniz C, Torres A. What's Next in Pneumonia? Arch Bronconeumol 2022;58:208-10. [PMID: 35312596 DOI: 10.1016/j.arbres.2021.08.006] [Reference Citation Analysis]
252 Yoon JH, Pinsky MR, Clermont G. Artificial Intelligence in Critical Care Medicine. Crit Care 2022;26:75. [PMID: 35337366 DOI: 10.1186/s13054-022-03915-3] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 11.0] [Reference Citation Analysis]
253 Lipták P, Banovcin P, Rosoľanka R, Prokopič M, Kocan I, Žiačiková I, Uhrik P, Grendar M, Hyrdel R. A machine learning approach for identification of gastrointestinal predictors for the risk of COVID-19 related hospitalization. PeerJ 2022;10:e13124. [PMID: 35341062 DOI: 10.7717/peerj.13124] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
254 Yang L, Wang SH, Zhang YD. EDNC: Ensemble Deep Neural Network for COVID-19 Recognition. Tomography 2022;8:869-90. [PMID: 35314648 DOI: 10.3390/tomography8020071] [Cited by in Crossref: 5] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
255 Monday HN, Li J, Nneji GU, Hossin MA, Nahar S, Jackson J, Chikwendu IA. WMR-DepthwiseNet: A Wavelet Multi-Resolution Depthwise Separable Convolutional Neural Network for COVID-19 Diagnosis. Diagnostics 2022;12:765. [DOI: 10.3390/diagnostics12030765] [Reference Citation Analysis]
256 Ishiwata Y, Miura K, Kishimoto M, Nomura K, Sawamura S, Magami S, Ikawa M, Yamashiro T, Utsunomiya D. Comparison of CO-RADS Scores Based on Visual and Artificial Intelligence Assessments in a Non-Endemic Area. Diagnostics 2022;12:738. [DOI: 10.3390/diagnostics12030738] [Reference Citation Analysis]
257 Punn NS, Agarwal S. CHS-Net: A Deep Learning Approach for Hierarchical Segmentation of COVID-19 via CT Images. Neural Process Lett. [DOI: 10.1007/s11063-022-10785-x] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
258 Ahsan MM, Luna SA, Siddique Z. Machine-Learning-Based Disease Diagnosis: A Comprehensive Review. Healthcare 2022;10:541. [DOI: 10.3390/healthcare10030541] [Cited by in Crossref: 24] [Cited by in F6Publishing: 19] [Article Influence: 24.0] [Reference Citation Analysis]
259 Gunraj H, Sabri A, Koff D, Wong A. COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 From Chest CT Images Through Bigger, More Diverse Learning. Front Med 2022;8:729287. [DOI: 10.3389/fmed.2021.729287] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 10.0] [Reference Citation Analysis]
260 Montalbo FJ. Truncating fined-tuned vision-based models to lightweight deployable diagnostic tools for SARS-CoV-2 infected chest X-rays and CT-scans. Multimed Tools Appl 2022;81:16411-39. [PMID: 35261555 DOI: 10.1007/s11042-022-12484-0] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
261 Mohammad M, Swapna B. COVID-19 Diagnosis with HRCT Images Using Deep Transfer Learning. 2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) 2022. [DOI: 10.1109/icaitpr51569.2022.9844195] [Reference Citation Analysis]
262 Shoaib MR, Emara HM, Elwekeil M, El-shafai W, Taha TE, El-fishawy AS, El-rabaie EM, El-samie FEA. Hybrid classification structures for automatic COVID-19 detection. J Ambient Intell Human Comput. [DOI: 10.1007/s12652-021-03686-9] [Reference Citation Analysis]
263 Wang Y, Tsai D, Yen L, Yao Y, Chiang T, Chiu C, Lin T, Yeh K, Chang F. Clinical Characteristics of COVID-19 Patients and Application to an Artificial Intelligence System for Disease Surveillance. JCM 2022;11:1437. [DOI: 10.3390/jcm11051437] [Reference Citation Analysis]
264 Elsayed O, Mansour W. Digital Transformation and Health Systems Performance in Global Settings During COVID-19. Advances in Human Services and Public Health 2022. [DOI: 10.4018/978-1-7998-8973-1.ch003] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
265 Nira, Kumar H. Epidemiological Mucormycosis treatment and diagnosis challenges using the adaptive properties of computer vision techniques based approach: a review. Multimed Tools Appl 2022;81:14217-45. [PMID: 35233180 DOI: 10.1007/s11042-022-12450-w] [Reference Citation Analysis]
266 Bartoli A, Fournel J, Maurin A, Marchi B, Habert P, Castelli M, Gaubert J, Cortaredona S, Lagier J, Million M, Raoult D, Ghattas B, Jacquier A. Value and prognostic impact of a deep learning segmentation model of COVID-19 lung lesions on low-dose chest CT. Research in Diagnostic and Interventional Imaging 2022;1:100003. [DOI: 10.1016/j.redii.2022.100003] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
267 Panahi A, Askari Moghadam R, Akrami M, Madani K. Deep Residual Neural Network for COVID-19 Detection from Chest X-ray Images. SN Comput Sci 2022;3:169. [PMID: 35224513 DOI: 10.1007/s42979-022-01067-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
268 Absar N, Mamur B, Mahmud A, Emran TB, Khandaker MU, Faruque M, Osman H, Elzaki A, Elkhader BA. Development of a computer-aided tool for detection of COVID-19 pneumonia from CXR images using machine learning algorithm. Journal of Radiation Research and Applied Sciences 2022;15:32-43. [DOI: 10.1016/j.jrras.2022.02.002] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
269 Chen Z, Liu J, Zhu M, Woo PY, Yuan Y. Instance Importance-Aware Graph Convolutional Network for 3D Medical Diagnosis. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102421] [Reference Citation Analysis]
270 Bourdoncle S, Eche T, Mcgale J, Yiu K, Partouche E, Yeh R, Ammari S, Rousseau H, Dercle L, Mokrane F. Investigating of the role of CT scan for cancer patients during the first wave of COVID-19 pandemic. Research in Diagnostic and Interventional Imaging 2022;1:100004. [DOI: 10.1016/j.redii.2022.100004] [Reference Citation Analysis]
271 Fang Z, Ren J, Maclellan C, Li H, Zhao H, Hussain A, Fortino G. A Novel Multi-Stage Residual Feature Fusion Network for Detection of COVID-19 in Chest X-Ray Images. IEEE Trans Mol Biol Multi-Scale Commun 2022;8:17-27. [DOI: 10.1109/tmbmc.2021.3099367] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 8.0] [Reference Citation Analysis]
272 Ruano J, Arcila J, Romo-bucheli D, Vargas C, Rodríguez J, Mendoza Ó, Plazas M, Bautista L, Villamizar J, Pedraza G, Moreno A, Valenzuela D, Vázquez L, Valenzuela-santos C, Camacho P, Mantilla D, Martínez Carrillo F. Deep learning representations to support COVID-19 diagnosis on CT slices. biomedica 2022;42:170-183. [DOI: 10.7705/biomedica.5927] [Reference Citation Analysis]
273 Hassan H, Ren Z, Zhou C, Khan MA, Pan Y, Zhao J, Huang B. Supervised and Weakly Supervised Deep Learning Models for COVID-19 CT Diagnosis: A Systematic Review. Computer Methods and Programs in Biomedicine 2022. [DOI: 10.1016/j.cmpb.2022.106731] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
274 Quazi S, Saha RP, Singh MK. Applications of Artificial Intelligence in Healthcare. J Exp Bio & Ag Sci 2022;10:211-226. [DOI: 10.18006/2022.10(1).211.226] [Reference Citation Analysis]
275 Choudhury S, Chohan A, Dadhwal R, Vakil AP, Franco R, Taweesedt PT. Applications of artificial intelligence in common pulmonary diseases. Artif Intell Med Imaging 2022; 3(1): 1-7 [DOI: 10.35711/aimi.v3.i1.1] [Reference Citation Analysis]
276 Sani S, Shermeh HE. A novel algorithm for detection of COVID-19 by analysis of chest CT images using Hopfield neural network. Expert Syst Appl 2022;:116740. [PMID: 35228781 DOI: 10.1016/j.eswa.2022.116740] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
277 Monday HN, Li J, Nneji GU, Nahar S, Hossin MA, Jackson J. COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network. Healthcare 2022;10:422. [DOI: 10.3390/healthcare10030422] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
278 Nneji GU, Deng J, Monday HN, Hossin MA, Obiora S, Nahar S, Cai J. COVID-19 Identification from Low-Quality Computed Tomography Using a Modified Enhanced Super-Resolution Generative Adversarial Network Plus and Siamese Capsule Network. Healthcare (Basel) 2022;10:403. [PMID: 35207017 DOI: 10.3390/healthcare10020403] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
279 Soya E, Ekenel N, Savas R, Toprak T, Bewes J, Doganay O. Pixel-based analysis of pulmonary changes on CT lung images due to COVID-19 pneumonia. CSDM 2022;12:6. [DOI: 10.25259/jcis_172_2021] [Reference Citation Analysis]
280 Xiong Y, Ma Y, Ruan L, Li D, Lu C, Huang L; National Traditional Chinese Medicine Medical Team. Comparing different machine learning techniques for predicting COVID-19 severity. Infect Dis Poverty 2022;11:19. [PMID: 35177120 DOI: 10.1186/s40249-022-00946-4] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
281 De Lucia F, Amer Ouali R, Devriendt A, Sanoussi S, Cannie M. Comparison of Chest Computed Tomography Between the Two Waves of Coronavirus Disease 2019 in Belgium Using Artificial Intelligence. Cureus. [DOI: 10.7759/cureus.22203] [Reference Citation Analysis]
282 Kumar N, Hashmi A, Gupta M, Kundu A. Automatic Diagnosis of Covid-19 Related Pneumonia from CXR and CT-Scan Images. Eng Technol Appl Sci Res 2022;12:7993-7. [DOI: 10.48084/etasr.4613] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
283 Thomas LB, Mastorides SM, Viswanadhan NA, Jakey CE, Borkowski AA. Artificial Intelligence: Review of Current and Future Applications in Medicine. Fed Pract 2021;38:527-38. [PMID: 35136337 DOI: 10.12788/fp.0174] [Reference Citation Analysis]
284 Das D, Ghosal S, Mohanty SP. CoviLearn: A Machine Learning Integrated Smart X-Ray Device in Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19. SN Comput Sci 2022;3:150. [PMID: 35132394 DOI: 10.1007/s42979-022-01035-x] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
285 Aria M, Nourani E, Golzari Oskouei A, Liu J. ADA-COVID: Adversarial Deep Domain Adaptation-Based Diagnosis of COVID-19 from Lung CT Scans Using Triplet Embeddings. Computational Intelligence and Neuroscience 2022;2022:1-17. [DOI: 10.1155/2022/2564022] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
286 Krauze AV, Zhuge Y, Zhao R, Tasci E, Camphausen K. AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models. J Biotechnol Biomed 2022;5:1-19. [PMID: 35106480 DOI: 10.26502/jbb.2642-91280046] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
287 Ortiz A, Trivedi A, Desbiens J, Blazes M, Robinson C, Gupta S, Dodhia R, Bhatraju PK, Liles WC, Lee A, Ferres JML. Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes. Sci Rep 2022;12:1716. [PMID: 35110593 DOI: 10.1038/s41598-022-05532-0] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
288 Onnis C, Muscogiuri G, Paolo Bassareo P, Cau R, Mannelli L, Cadeddu C, Suri JS, Cerrone G, Gerosa C, Sironi S, Faa G, Carriero A, Pontone G, Saba L. Non-invasive coronary imaging in patients with COVID-19: a narrative review. European Journal of Radiology 2022. [DOI: 10.1016/j.ejrad.2022.110188] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
289 Ali Ahmed SA, Yavuz MC, Sen MU, Gulsen F, Tutar O, Korkmazer B, Samanci C, Şirolu S, Hamid R, Eryürekli AE, Mammadov T, Yanikoglu B. Comparison and Ensemble of 2D and 3D Approaches for COVID-19 Detection in CT Images. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.02.018] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
290 Wang Z, Dong J, Zhang J. Multi-Model Ensemble Deep Learning Method to Diagnose COVID-19 Using Chest Computed Tomography Images. J Shanghai Jiaotong Univ (Sci ) 2022;27:70-80. [DOI: 10.1007/s12204-021-2392-3] [Reference Citation Analysis]
291 Bridge J, Meng Y, Zhu W, Fitzmaurice T, Mccann C, Addison C, Wang M, Merritt C, Franks S, Mackey M, Messenger S, Sun R, Zhao Y, Zheng Y. Development and External Validation of a Mixed-Effects Deep Learning Model to Diagnose COVID-19 from CT Imaging.. [DOI: 10.1101/2022.01.28.22270005] [Reference Citation Analysis]
292 . Unusual presentation of coronavirus disease2019(COVID-19): two cases of acute abdomen. HSI Journal 2022;2:277-280. [DOI: 10.46829/hsijournal.2021.12.2.2.277-280] [Reference Citation Analysis]
293 Li S, Guo Z, Lin J, Ying S, Mehta S. Artificial Intelligence for Classifying and Archiving Orthodontic Images. BioMed Research International 2022;2022:1-11. [DOI: 10.1155/2022/1473977] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
294 Ishwerlal RD, Agarwal R, Sujatha K. Radiographic Image Processing Analysis for Lung Infection - A Review. 2022 International Conference on Computer Communication and Informatics (ICCCI) 2022. [DOI: 10.1109/iccci54379.2022.9741011] [Reference Citation Analysis]
295 Lee Y, Kim YS, Lee DI, Jeong S, Kang GH, Jang YS, Kim W, Choi HY, Kim JG, Choi SH. The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection. Sci Rep 2022;12:1234. [PMID: 35075153 DOI: 10.1038/s41598-022-05069-2] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
296 Yuan L, Chen J, Feng H, Lv J, Lu X, Ji M. Early Identification of COVID-19 Progression to Its Severe Form Using Artificial Intelligence. Iran J Radiol 2022;19. [DOI: 10.5812/iranjradiol.112562] [Reference Citation Analysis]
297 Bisen RG, Pande NS, Rajurkar AM. The Role of Medical Imaging in COVID-19 Detection and Diagnosis: A Review. 2022 International Conference for Advancement in Technology (ICONAT) 2022. [DOI: 10.1109/iconat53423.2022.9725885] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
298 Yousef R, Gupta G, Yousef N, Khari M. A holistic overview of deep learning approach in medical imaging. Multimedia Systems. [DOI: 10.1007/s00530-021-00884-5] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
299 Laino ME, Ammirabile A, Lofino L, Lundon DJ, Chiti A, Francone M, Savevski V. Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence. Emerg Radiol 2022. [PMID: 35048222 DOI: 10.1007/s10140-021-02008-y] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
300 Syarif A, Azman N, Ronal Repi VV, Sinaga E, Asvial M. UNAS-Net: A deep convolutional neural network for predicting Covid-19 severity. Inform Med Unlocked 2022;28:100842. [PMID: 35018298 DOI: 10.1016/j.imu.2021.100842] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
301 Irmak E. COVID-19 disease diagnosis from paper-based ECG trace image data using a novel convolutional neural network model. Phys Eng Sci Med 2022. [PMID: 35020175 DOI: 10.1007/s13246-022-01102-w] [Reference Citation Analysis]
302 Shastri S, Kansal I, Kumar S, Singh K, Popli R, Mansotra V. CheXImageNet: a novel architecture for accurate classification of Covid-19 with chest x-ray digital images using deep convolutional neural networks. Health Technol (Berl) 2022;:1-12. [PMID: 35036283 DOI: 10.1007/s12553-021-00630-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
303 Ahsan MM, Ahad MT, Soma FA, Paul S, Chowdhury A, Luna SA, Yazdan MMS, Rahman A, Siddique Z, Huebner P. Detecting SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence. IEEE Access 2021;9:35501-13. [PMID: 34976572 DOI: 10.1109/ACCESS.2021.3061621] [Cited by in Crossref: 20] [Cited by in F6Publishing: 21] [Article Influence: 20.0] [Reference Citation Analysis]
304 Danilov VV, Proutski A, Karpovsky A, Kirpich A, Litmanovich D, Nefaridze D, Talalov O, Semyonov S, Koniukhovskii V, Shvartc V, Gankin Y. Indirect supervision applied to COVID-19 and pneumonia classification. Inform Med Unlocked 2022;28:100835. [PMID: 34977331 DOI: 10.1016/j.imu.2021.100835] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
305 Kanwal S, Khan F, Alamri S, Dashtipur K, Gogate M. COVID‐opt‐aiNet : A clinical decision support system for COVID ‐19 detection. Int J Imaging Syst Tech. [DOI: 10.1002/ima.22695] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
306 Peng Y, Liu E, Peng S, Chen Q, Li D, Lian D. Using artificial intelligence technology to fight COVID-19: a review. Artif Intell Rev 2022;:1-37. [PMID: 35002010 DOI: 10.1007/s10462-021-10106-z] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
307 Kannan R, Shing KW, Ramakrishnan K, Ong HB, Alamsyah A. Machine Learning Models for Predicting Financially Vigilant Low-Income Households. IEEE Access 2022;10:70418-27. [DOI: 10.1109/access.2022.3187564] [Reference Citation Analysis]
308 Roberts A, Chouhan RS, Shahdeo D, Shrikrishna NS, Kesarwani V, Horvat M, Gandhi S. A Recent Update on Advanced Molecular Diagnostic Techniques for COVID-19 Pandemic: An Overview. Front Immunol 2021;12:732756. [PMID: 34970254 DOI: 10.3389/fimmu.2021.732756] [Cited by in Crossref: 12] [Cited by in F6Publishing: 15] [Article Influence: 12.0] [Reference Citation Analysis]
309 Shriram R, Kumar TRK, Samuktha V, Karthika R. General Adversarial Networks: A Tool to Detect the Novel Coronavirus from CT Scans. International Conference on Artificial Intelligence for Smart Community 2022. [DOI: 10.1007/978-981-16-2183-3_21] [Reference Citation Analysis]
310 Cau R, Faa G, Nardi V, Balestrieri A, Puig J, Suri JS, Sanfilippo R, Saba L. Long-COVID diagnosis: from diagnostic to advanced AI-driven models. European Journal of Radiology 2022. [DOI: 10.1016/j.ejrad.2022.110164] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 10.0] [Reference Citation Analysis]
311 Zheng Y, Dong H. The Deep Learning-Based Framework for Automated Predicting COVID-19 Severity Score. Procedia Comput Sci 2022;207:1096-104. [PMID: 36275389 DOI: 10.1016/j.procs.2022.09.165] [Reference Citation Analysis]
312 He X, Ying G, Zhang J, Chu X. Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT Classification. Lecture Notes in Computer Science 2022. [DOI: 10.1007/978-3-031-16431-6_53] [Reference Citation Analysis]
313 Nagib AE, Saeed MM, El-feky SF, Mohamed AK. Hyperparameters Optimization of Deep Convolutional Neural Network for Detecting COVID-19 Using Differential Evolution. International Series in Operations Research & Management Science 2022. [DOI: 10.1007/978-3-030-87019-5_18] [Reference Citation Analysis]
314 Awotunde JB, Jimoh RG, Matiluko OE, Gbadamosi B, Ajamu GJ. Artificial Intelligence and an Edge-IoMT-Based System for Combating COVID-19 Pandemic. Intelligent Interactive Multimedia Systems for e-Healthcare Applications 2022. [DOI: 10.1007/978-981-16-6542-4_11] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
315 Abuhamdah A, Jaradat GM, Alsmadi M. Deep Learning for COVID-19 Cases-Based XCR and Chest CT Images. Advances on Smart and Soft Computing 2022. [DOI: 10.1007/978-981-16-5559-3_24] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
316 Sahoo NK. Study on the ANN Forecasting of Epidemical Diseases. Materials Horizons: From Nature to Nanomaterials 2022. [DOI: 10.1007/978-981-16-4372-9_8] [Reference Citation Analysis]
317 Maganaris C, Protopapadakis E, Bakalos N, Doulamis N, Kalogeras D, Angeli A. Transferability Limitations for Covid 3D Localization Using SARS-CoV-2 Segmentation Models in 4D CT Images. Advances in Visual Computing 2022. [DOI: 10.1007/978-3-031-20716-7_25] [Reference Citation Analysis]
318 Adetunji CO, Olaniyan OT, Adeyomoye O, Dare A, Adeniyi MJ, Alex E, Rebezov M, Petukhova E, Shariati MA. Machine Learning Approaches for COVID-19 Pandemic. Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis 2022. [DOI: 10.1007/978-3-030-79753-9_8] [Reference Citation Analysis]
319 Vernuccio F, Cutaia G, Cannella R, Vernuccio L, Lagalla R, Midiri M. Chest CT in COVID-19 Pneumonia: Potentials and Limitations of Radiomics and Artificial Intelligence. Understanding COVID-19: The Role of Computational Intelligence 2022. [DOI: 10.1007/978-3-030-74761-9_3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
320 Dhanda N, Iqram S. Artificial intelligence. Biomedical Engineering Applications for People with Disabilities and the Elderly in the COVID-19 Pandemic and Beyond 2022. [DOI: 10.1016/b978-0-323-85174-9.00001-7] [Reference Citation Analysis]
321 Thangam D, Malali AB, Subramaniyan G, Mariappan S, Mohan S, Park JY. Relevance of Artificial Intelligence in Modern Healthcare. Integrating AI in IoT Analytics on the Cloud for Healthcare Applications 2022. [DOI: 10.4018/978-1-7998-9132-1.ch005] [Reference Citation Analysis]
322 Nayak CB, Nanda PK, Tripathy S, Swain SC, Das CK, Sahu R. The economic impact of covid-19 and the role of AI. Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 2022. [DOI: 10.1016/b978-0-323-90054-6.00002-7] [Reference Citation Analysis]
323 Rezayi S, Ghazisaeedi M, Kalhori SN, Saeedi S. Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19. J Med Signals Sens 2022;12:233. [DOI: 10.4103/jmss.jmss_111_21] [Reference Citation Analysis]
324 Zhang Y, Hua J, Adu Gyamfi B, Shaw R. Artificial Intelligence and Its Importance in Post-COVID-19 China. Considerations for a Post-COVID-19 Technology and Innovation Ecosystem in China 2022. [DOI: 10.1007/978-981-16-6959-0_8] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
325 Gu X, Chen S, Tong X, Yan H, Chen L, Wu S. Review of Covid-19 Diagnosis Techniques Combined with Machine Learning and AI Analysis. IoT and Big Data Technologies for Health Care 2022. [DOI: 10.1007/978-3-030-94182-6_41] [Reference Citation Analysis]
326 Shoaib M, Aqib AI, Bhutta ZA, Pu W, Muzammil I, Naseer MA. Computational Intelligence-Based Diagnosis of COVID-19. Computational Intelligence for COVID-19 and Future Pandemics 2022. [DOI: 10.1007/978-981-16-3783-4_11] [Reference Citation Analysis]
327 He Z, Hua J, Zhang Y, Deng J, Adu-gyamfi B, Shaw R. Reflections on pandemic governance in China and its implications to future 5G strategy. Pandemic Risk, Response, and Resilience 2022. [DOI: 10.1016/b978-0-323-99277-0.00020-6] [Reference Citation Analysis]
328 Glauner P. Künstliche Intelligenz im Gesundheitswesen: Grundlagen, Möglichkeiten und Herausforderungen. Innovationen im Gesundheitswesen 2022. [DOI: 10.1007/978-3-658-33801-5_8] [Reference Citation Analysis]
329 Aruna Kumar SV, Nagashree S, Mahanand BS. Detection of COVID-19 Using a Multi-scale Deep Learning Network: Covid-MSNet. Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis 2022. [DOI: 10.1007/978-981-19-1076-0_21] [Reference Citation Analysis]
330 Majhi B, Thangeda R, Majhi R. A Review on Detection of COVID-19 Patients Using Deep Learning Techniques. Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis 2022. [DOI: 10.1007/978-3-030-79753-9_4] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
331 Rhalem W, Raji M, Aqili N, Mhamdi JE, Allali I, Kharmoum N, Retal S, Hammouch A, Laghrissi A, Ghazal H. Digital Technology und Artificial Intelligence Facing COVID-19. Advanced Intelligent Systems for Sustainable Development (AI2SD’2020) 2022. [DOI: 10.1007/978-3-030-90639-9_102] [Reference Citation Analysis]
332 Basir MT, Abbas SR. Applications of digital and smart technologies to control SARS-CoV-2 transmission, rapid diagnosis, and monitoring. Biotechnology in Healthcare 2022. [DOI: 10.1016/b978-0-323-90042-3.25001-9] [Reference Citation Analysis]
333 Fan X, Feng X, Dong Y, Hou H. COVID-19 CT Image Recognition Algorithm Based on Transformer and CNN. Displays 2022. [DOI: 10.1016/j.displa.2022.102150] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 7.0] [Reference Citation Analysis]
334 Adiban Afkham S, Hessami A, Saghazadeh A, Rezaei N. An overview of possible solutions putting an end to the COVID-19 pandemic. Acta Biomed 2022;93:e2022202. [PMID: 35546013 DOI: 10.23750/abm.v93i2.12130] [Reference Citation Analysis]
335 Rodríguez-garcía I, Sánchez-pastor T, Vázquez-escobar J, Gómez-gonzález JL, Cárdenas-montes M. Uncertainty Propagation and Salient Features Maps in Deep Learning Architectures for Supporting Covid-19 Diagnosis. Artificial Intelligence and Machine Learning Methods in COVID-19 and Related Health Diseases 2022. [DOI: 10.1007/978-3-031-04597-4_1] [Reference Citation Analysis]
336 Biswas A, Samanta PK. Supervised Machine Learning Approach for the Prediction of COVID-19 Cases. Lecture Notes in Electrical Engineering 2022. [DOI: 10.1007/978-981-16-9154-6_56] [Reference Citation Analysis]
337 Yoon JH, Pinsky MR, Clermont G. Artificial Intelligence in Critical Care Medicine. Annual Update in Intensive Care and Emergency Medicine 2022. [DOI: 10.1007/978-3-030-93433-0_27] [Reference Citation Analysis]
338 Sahoo SK, Sekar V, Siddi S, Kumar M. A, Yuvaraj T, Kshirsagar PR. Identification of people affected from Corona virus using artificial intelligence. RECENT TRENDS IN SCIENCE AND ENGINEERING 2022. [DOI: 10.1063/5.0074173] [Reference Citation Analysis]
339 Lakshmi J. Deep learning on medical image analysis on COVID-19 x-ray dataset using an X-Net architecture. Deep Learning for Medical Applications with Unique Data 2022. [DOI: 10.1016/b978-0-12-824145-5.00011-3] [Reference Citation Analysis]
340 Sharma V, Dastidar MG, Sutradhar S, Raj V, De Silva K, Roy S. A step toward better sample management of COVID-19: On-spot detection by biometric technology and artificial intelligence. COVID-19 and the Sustainable Development Goals 2022. [DOI: 10.1016/b978-0-323-91307-2.00017-1] [Reference Citation Analysis]
341 Storey VC, Lukyanenko R, Grange C. Physically Distancing Humans With An App for That: Physically Distancing Mangement Technologies. Journal of Database Management 2022;33:1-16. [DOI: 10.4018/jdm.305731] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
342 Kaushal V, Gupta R. Role of Artificial Intelligence in Diagnosis of Infectious Diseases. Biomedical Translational Research 2022. [DOI: 10.1007/978-981-16-4345-3_8] [Reference Citation Analysis]
343 Orimoloye IR, Ololade OO, Ekundayo OY, Busayo ET, Afuye GA, Kalumba AM, Ekundayo TC. Assessment of global research trends in the application of data science and deep and machine learning to the COVID-19 pandemic. Data Science for COVID-19 2022. [DOI: 10.1016/b978-0-323-90769-9.00030-x] [Reference Citation Analysis]
344 Arora T. Machine Learning-Based Categorization of COVID-19 Patients. Applications of Computational Science in Artificial Intelligence 2022. [DOI: 10.4018/978-1-7998-9012-6.ch010] [Reference Citation Analysis]
345 Fazle Rabbi M, Mahedy Hasan SM, Champa AI, Rifat Hossain M, Asif Zaman M. A Convolutional Neural Network Model for Screening COVID-19 Patients Based on CT Scan Images. Lecture Notes on Data Engineering and Communications Technologies 2022. [DOI: 10.1007/978-981-16-6636-0_12] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
346 Vaibhav Ram SVNS, Dhanalakshmi S. Pulmonary Image Analysis with Computer Vision Using Binary Classification Method for COVID-19 Detection. Proceedings of International Conference on Recent Trends in Computing 2022. [DOI: 10.1007/978-981-16-7118-0_41] [Reference Citation Analysis]
347 Yang H, Zhang S, Liu R, Krall A, Wang Y, Ventura M, Deflitch C. Epidemic Informatics and Control: A Review from System Informatics to Epidemic Response and Risk Management in Public Health. Springer Proceedings in Business and Economics 2022. [DOI: 10.1007/978-3-030-75166-1_1] [Reference Citation Analysis]
348 Hussain A, Imad M, Khan A, Ullah B. Multi-class Classification for the Identification of COVID-19 in X-Ray Images Using Customized Efficient Neural Network. AI and IoT for Sustainable Development in Emerging Countries 2022. [DOI: 10.1007/978-3-030-90618-4_23] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
349 Kumar MJD, Santhosh G, Niranjajn P, Manasa GR. A Review on Effectiveness of AI and ML Techniques for Classification of COVID-19 Medical Images. Advances in Intelligent Systems and Computing 2022. [DOI: 10.1007/978-981-16-3342-3_14] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
350 Das N, Topalovic M, Janssens W. AIM in Respiratory Disorders. Artificial Intelligence in Medicine 2022. [DOI: 10.1007/978-3-030-64573-1_178] [Reference Citation Analysis]
351 Hashiguchi TCO, Oderkirk J, Slawomirski L. Fulfilling the Promise of Artificial Intelligence in the Health Sector: Let’s Get Real. Value in Health 2022. [DOI: 10.1016/j.jval.2021.11.1369] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
352 Jalali Moghaddam M, Ghavipour M. Towards smart diagnostic methods for COVID-19: Review of deep learning for medical imaging. IPEM Transl 2022;3:100008. [PMID: 36312890 DOI: 10.1016/j.ipemt.2022.100008] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
353 Shazia A, Xuan TZ, Chuah JH, Mohafez H, Lai KW. Detection of COVID-19 on Chest X-Ray Using Neural Networks. 6th Kuala Lumpur International Conference on Biomedical Engineering 2021 2022. [DOI: 10.1007/978-3-030-90724-2_45] [Reference Citation Analysis]
354 Kapoor A. Use of artificial intelligence on chest skiagrams in patients with COVID-19: Time to widen the horizon. Cancer Res Stat Treat 2022;5:116. [DOI: 10.4103/crst.crst_39_22] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
355 Yang B, Chen X, Yang Q, He H, Wang C, Peng Z, Liu Y, Wang P, Wu J. Computed tomography-aided diagnosis of COVID-19. Radiol Infect Dis 2022;9:62. [DOI: 10.4103/rid.rid_23_22] [Reference Citation Analysis]
356 Santosh K, Das N, Ghosh S. COVID-19: prediction, screening, and decision-making. Deep Learning Models for Medical Imaging 2022. [DOI: 10.1016/b978-0-12-823504-1.00015-5] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
357 Liu Z, Chen X, Carter W, Moruf A, Komatsu TE, Pahwa S, Chan-tack K, Snyder K, Petrick N, Cha K, Lal-nag M, Hatim Q, Thakkar S, Lin Y, Huang R, Wang D, Patterson TA, Tong W. AI-powered drug repurposing for developing COVID-19 treatments. Reference Module in Biomedical Sciences 2022. [DOI: 10.1016/b978-0-12-824010-6.00005-8] [Reference Citation Analysis]
358 Tricarico D, Chaudhry HAH, Fiandrotti A, Grangetto M. Deep Regression by Feature Regularization for COVID-19 Severity Prediction. Lecture Notes in Computer Science 2022. [DOI: 10.1007/978-3-031-13324-4_42] [Reference Citation Analysis]
359 Banerjee A, Paul S. A Predictive Analysis for Diagnosis of COVID-19, Pneumonia and Lung Cancer Using Deep Learning. Intelligent Healthcare 2022. [DOI: 10.1007/978-981-16-8150-9_8] [Reference Citation Analysis]
360 Kardos AS, Simon J, Nardocci C, Szabó IV, Nagy N, Abdelrahman RH, Zsarnóczay E, Fejér B, Futácsi B, Müller V, Merkely B, Maurovich-Horvat P. The diagnostic performance of deep-learning-based CT severity score to identify COVID-19 pneumonia. Br J Radiol 2022;95:20210759. [PMID: 34889645 DOI: 10.1259/bjr.20210759] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
361 Bellini V, Cascella M, Cutugno F, Russo M, Lanza R, Compagnone C, Bignami EG. Understanding basic principles of Artificial Intelligence: a practical guide for intensivists. Acta Biomed 2022;93:e2022297. [PMID: 36300214 DOI: 10.23750/abm.v93i5.13626] [Reference Citation Analysis]
362 Bhaskar SMM. The Impact of the COVID-19 Pandemic on e-Services and Digital Tools Development in Medicine. Contemporary Cardiology 2022. [DOI: 10.1007/978-3-031-15478-2_25] [Reference Citation Analysis]
363 Chaudhuri R, Nagpal D, Azad A, Pal S. WE-Net: An Ensemble Deep Learning Model for Covid-19 Detection in Chest X-ray Images Using Segmentation and Classification. Communications in Computer and Information Science 2022. [DOI: 10.1007/978-3-031-12641-3_10] [Reference Citation Analysis]
364 Ahmed MM, Sayed AM, Khafagy GM, El Sayed IT, Elkholy YS, Fares AH, Hasan MD, El Nahas HG, Sarhan MD, Raslan EI, Elsayed RM, Sayed AA, Elmeshmeshy EI, Yassen RM, Tawfik NM, Hussein HA, Gaber DM, Shehata MM, Fares S. Accuracy of the Traditional COVID-19 Phone Triaging System and Phone Triage-Driven Deep Learning Model. J Prim Care Community Health 2022;13:21501319221113544. [PMID: 35869692 DOI: 10.1177/21501319221113544] [Reference Citation Analysis]
365 Gunturu LN, Dornadula G. Internet of Health Things (IoHT): The Significance of Virtual Tools Aiding to Overcome Novel Coronavirus (COVID-19) Pandemic. Computational Intelligence for COVID-19 and Future Pandemics 2022. [DOI: 10.1007/978-981-16-3783-4_2] [Reference Citation Analysis]
366 Sherif K, Gadallah YE, Ahmed K, Elsayed S, Mohamed AW. Role of Artificial Intelligence in Diagnosis of Covid-19 Using CT-Scan. International Series in Operations Research & Management Science 2022. [DOI: 10.1007/978-3-030-87019-5_4] [Reference Citation Analysis]
367 Snehlata, Teja KB, Mukherjee B. Application of CRISPR-Based Diagnostic Tools in Detecting SARS-CoV-2 Infection. COVID-19: Tackling Global Pandemics through Scientific and Social Tools 2022. [DOI: 10.1016/b978-0-323-85844-1.00002-7] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
368 Karthik R, Menaka R, Anand S, Johnson A, Srilakshmi K. Attention-Based Residual Learning Network for COVID-19 Detection Using Chest CT Images. International Series in Operations Research & Management Science 2022. [DOI: 10.1007/978-3-030-87019-5_21] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
369 Ganjali R, Eslami S, Samimi T, Sargolzaei M, Firouraghi N, MohammadEbrahimi S, Khoshrounejad F, Kheirdoust A. Clinical informatics solutions in COVID-19 pandemic: Scoping literature review. Inform Med Unlocked 2022;30:100929. [PMID: 35350124 DOI: 10.1016/j.imu.2022.100929] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
370 Caselli M, Fracasso A. COVID-19 and Technology. Handbook of Labor, Human Resources and Population Economics 2022. [DOI: 10.1007/978-3-319-57365-6_331-1] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
371 Kalpana, Srivastava A, Jha S. Data-driven machine learning: A new approach to process and utilize biomedical data. Predictive Modeling in Biomedical Data Mining and Analysis 2022. [DOI: 10.1016/b978-0-323-99864-2.00017-2] [Reference Citation Analysis]
372 Filev PD, Stillman AE. Structured Reporting in Medical Imaging: The Role of Artificial Intelligence. Artificial Intelligence in Cardiothoracic Imaging 2022. [DOI: 10.1007/978-3-030-92087-6_10] [Reference Citation Analysis]
373 Hossain MB, Iqbal SMHS, Islam MM, Akhtar MN, Sarker IH. Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images. Inform Med Unlocked 2022;30:100916. [PMID: 35342787 DOI: 10.1016/j.imu.2022.100916] [Cited by in Crossref: 13] [Cited by in F6Publishing: 8] [Article Influence: 13.0] [Reference Citation Analysis]
374 Ogundokun RO, Awotunde JB, Onawola P, Aro TO. LASSO-DT Based Classification Technique for Discovery of COVID-19 Disease Using Chest X-Ray Images. International Series in Operations Research & Management Science 2022. [DOI: 10.1007/978-3-030-87019-5_23] [Reference Citation Analysis]
375 Chen C, Li R, Shen H, Xia L. Long Short-Term Memory Based Framework for Longitudinal Assessment of COVID-19 Using CT Imaging and Laboratory Data. IEEE Access 2022;10:55533-45. [DOI: 10.1109/access.2022.3176883] [Reference Citation Analysis]
376 Kora P, Ooi CP, Faust O, Raghavendra U, Gudigar A, Chan WY, Meenakshi K, Swaraja K, Plawiak P, Rajendra Acharya U. Transfer learning techniques for medical image analysis: A review. Biocybernetics and Biomedical Engineering 2022;42:79-107. [DOI: 10.1016/j.bbe.2021.11.004] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 9.0] [Reference Citation Analysis]
377 Mohbey KK, Sharma S, Kumar S, Sharma M. COVID-19 identification and analysis using CT scan images: Deep transfer learning-based approach. Blockchain Applications for Healthcare Informatics 2022. [DOI: 10.1016/b978-0-323-90615-9.00011-6] [Reference Citation Analysis]
378 Hoang TNM, Son TT, Nghiem ND, Tuan LM. SSL-MedImNet: Self-Supervised Pre-training of Deep Neural Network for COVID-19 Diagnosis. Intelligence of Things: Technologies and Applications 2022. [DOI: 10.1007/978-3-031-15063-0_39] [Reference Citation Analysis]
379 de Santana MA, Gomes JC, de Freitas Barbosa VA, de Lima CL, Bandeira J, Valença MJS, de Souza RE, Masood AI, dos Santos WP. An Intelligent Tool to Support Diagnosis of Covid-19 by Texture Analysis of Computerized Tomography X-ray Images and Machine Learning. Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis 2022. [DOI: 10.1007/978-3-030-79753-9_15] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
380 Fayemiwo MA, Olowookere TA, Arekete SA, Ogunde AO, Odim MO, Oguntunde BO, Olaniyan OO, Ojewumi TO, Oyetade IS. Comparative Study and Detection of COVID-19 and Related Viral Pneumonia Using Fine-Tuned Deep Transfer Learning. Intelligent Systems Reference Library 2022. [DOI: 10.1007/978-3-030-76732-7_2] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
381 Wu K, Jelfs B, Ma X, Ke R, Tan X, Fang Q. Weakly-supervised lesion analysis with a CNN-based framework for COVID-19. Phys Med Biol 2021;66. [PMID: 34905733 DOI: 10.1088/1361-6560/ac4316] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
382 Zokaeinikoo M, Kazemian P, Mitra P, Kumara S. AIDCOV: An Interpretable Artificial Intelligence Model for Detection of COVID-19 from Chest Radiography Images. ACM Trans Manage Inf Syst 2021;12:1-20. [DOI: 10.1145/3466690] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
383 Mondal AK, Bhattacharjee A, Singla P, Prathosh AP. xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography. IEEE J Transl Eng Health Med 2022;10:1100110. [PMID: 34956741 DOI: 10.1109/JTEHM.2021.3134096] [Cited by in Crossref: 6] [Cited by in F6Publishing: 10] [Article Influence: 3.0] [Reference Citation Analysis]
384 Pankaja Lakshmi. P, Sivagami. M, Balaji. V. A novel LT-LBP based prediction model for COVID-CT images with Machine Learning. 2021 International Conference on Information Systems and Advanced Technologies (ICISAT) 2021. [DOI: 10.1109/icisat54145.2021.9678196] [Reference Citation Analysis]
385 Jungmann F, Müller L, Hahn F, Weustenfeld M, Dapper AK, Mähringer-Kunz A, Graafen D, Düber C, Schafigh D, Pinto Dos Santos D, Mildenberger P, Kloeckner R. Commercial AI solutions in detecting COVID-19 pneumonia in chest CT: not yet ready for clinical implementation? Eur Radiol 2021. [PMID: 34950973 DOI: 10.1007/s00330-021-08409-4] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
386 Zhang YH, Hu XF, Ma JC, Wang XQ, Luo HR, Wu ZF, Zhang S, Shi DJ, Yu YZ, Qiu XM, Zeng WB, Chen W, Wang J. Clinical Applicable AI System Based on Deep Learning Algorithm for Differentiation of Pulmonary Infectious Disease. Front Med (Lausanne) 2021;8:753055. [PMID: 34926501 DOI: 10.3389/fmed.2021.753055] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
387 Bao G, Chen H, Liu T, Gong G, Yin Y, Wang L, Wang X. COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment. Pattern Recognit 2022;124:108499. [PMID: 34924632 DOI: 10.1016/j.patcog.2021.108499] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
388 Hao J, Xie J, Liu R, Hao H, Ma Y, Yan K, Liu R, Zheng Y, Zheng J, Liu J, Zhang J, Zhao Y. Automatic Sequence-Based Network for Lung Diseases Detection in Chest CT. Front Oncol 2021;11:781798. [PMID: 34926297 DOI: 10.3389/fonc.2021.781798] [Reference Citation Analysis]
389 Gillman AG, Lunardo F, Prinable J, Belous G, Nicolson A, Min H, Terhorst A, Dowling JA. Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review. Phys Eng Sci Med 2021. [PMID: 34919204 DOI: 10.1007/s13246-021-01093-0] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
390 Wong PK, Yan T, Wang H, Chan IN, Wang J, Li Y, Ren H, Wong CH. Automatic detection of multiple types of pneumonia: Open dataset and a multi-scale attention network. Biomed Signal Process Control 2022;73:103415. [PMID: 34909050 DOI: 10.1016/j.bspc.2021.103415] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
391 Alathari MJA, Al Mashhadany Y, Mokhtar MHH, Burham N, Bin Zan MSD, A Bakar AA, Arsad N. Human Body Performance with COVID-19 Affectation According to Virus Specification Based on Biosensor Techniques. Sensors (Basel) 2021;21:8362. [PMID: 34960456 DOI: 10.3390/s21248362] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
392 M. Dessouky M, Sabbeh SF, Alshehri B. A Survey on Deep Learning and Machine Learning for COVID-19 Detection. The 5th International Conference on Future Networks & Distributed Systems 2021. [DOI: 10.1145/3508072.3508094] [Reference Citation Analysis]
393 Bai X, Wang H, Ma L, Xu Y, Gan J, Fan Z, Yang F, Ma K, Yang J, Bai S, Shu C, Zou X, Huang R, Zhang C, Liu X, Tu D, Xu C, Zhang W, Wang X, Chen A, Zeng Y, Yang D, Wang M, Holalkere N, Halin NJ, Kamel IR, Wu J, Peng X, Wang X, Shao J, Mongkolwat P, Zhang J, Liu W, Roberts M, Teng Z, Beer L, Sanchez LE, Sala E, Rubin DL, Weller A, Lasenby J, Zheng C, Wang J, Li Z, Schönlieb C, Xia T. Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence. Nat Mach Intell 2021;3:1081-9. [DOI: 10.1038/s42256-021-00421-z] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
394 Trovato G, Russo M. Artificial Intelligence (AI) and Lung Ultrasound in Infectious Pulmonary Disease. Front Med (Lausanne) 2021;8:706794. [PMID: 34901048 DOI: 10.3389/fmed.2021.706794] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
395 Sun K, Zhu J, Yuan H, Li D, Wang C, Wang Z. CT Image Classification and Detection of COVID-19 Based on Convolutional Neural Network. 2021 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE) 2021. [DOI: 10.1109/rasse53195.2021.9686902] [Reference Citation Analysis]
396 Alalawi H, Alsuwat M, Alhakami H. A Survey of the Application of Artifical Intellegence on COVID-19 Diagnosis and Prediction. Eng Technol Appl Sci Res 2021;11:7824-35. [DOI: 10.48084/etasr.4503] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
397 Haq AU, Li JP, Ahmad S, Khan S, Alshara MA, Alotaibi RM. Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare. Sensors (Basel) 2021;21:8219. [PMID: 34960313 DOI: 10.3390/s21248219] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
398 Shi J, Yi H, Ruan S, Wang Z, Hao X, An H, Wei W. DARNet: Dual-Attention Residual Network for Automatic Diagnosis of COVID-19 via CT Images. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2021. [DOI: 10.1109/bibm52615.2021.9669805] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
399 Katar O, Duman E. Deep Learning Based Covid-19 Detection With A Novel CT Images Dataset: EFSCH-19. European Journal of Science and Technology 2021. [DOI: 10.31590/ejosat.1021030] [Reference Citation Analysis]
400 Yang F, Tang ZR, Chen J, Tang M, Wang S, Qi W, Yao C, Yu Y, Guo Y, Yu Z. Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning. BMC Med Imaging 2021;21:189. [PMID: 34879818 DOI: 10.1186/s12880-021-00723-z] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
401 Eche T, Schwartz LH, Mokrane FZ, Dercle L. Toward Generalizability in the Deployment of Artificial Intelligence in Radiology: Role of Computation Stress Testing to Overcome Underspecification. Radiol Artif Intell 2021;3:e210097. [PMID: 34870222 DOI: 10.1148/ryai.2021210097] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]
402 Marateb HR, Ziaie Nezhad F, Mohebian MR, Sami R, Haghjooy Javanmard S, Dehghan Niri F, Akafzadeh-Savari M, Mansourian M, Mañanas MA, Wolkewitz M, Binder H. Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study. Front Med (Lausanne) 2021;8:768467. [PMID: 34869483 DOI: 10.3389/fmed.2021.768467] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
403 Guo X, Lei Y, He P, Zeng W, Yang R, Ma Y, Feng P, Lyu Q, Wang G, Shan H. An ensemble learning method based on ordinal regression for COVID-19 diagnosis from chest CT. Phys Med Biol 2021;66. [PMID: 34715678 DOI: 10.1088/1361-6560/ac34b2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
404 Ackall G, Elmzoudi M, Yuan R, Chen C. An Exploration into the Detection of COVID-19 from Chest X-ray Scans Using the xRGM-NET Convolutional Neural Network. Technologies 2021;9:98. [DOI: 10.3390/technologies9040098] [Reference Citation Analysis]
405 Tchagna Kouanou A, Mih Attia T, Feudjio C, Djeumo AF, Ngo Mouelas A, Nzogang MP, Tchito Tchapga C, Tchiotsop D. An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection. J Healthc Eng 2021;2021:4733167. [PMID: 34853669 DOI: 10.1155/2021/4733167] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
406 Monterde D, Carot-Sans G, Cainzos-Achirica M, Abilleira S, Coca M, Vela E, Clèries M, Valero-Bover D, Comin-Colet J, García-Eroles L, Pérez-Sust P, Arrufat M, Lejardi Y, Piera-Jiménez J. Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients. Risk Manag Healthc Policy 2021;14:4729-37. [PMID: 34849041 DOI: 10.2147/RMHP.S326132] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
407 Tan W, Guo H. Data Augmentation and CNN Classification For Automatic COVID-19 Diagnosis From CT-Scan Images On Small Dataset. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) 2021. [DOI: 10.1109/icmla52953.2021.00234] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
408 Mu N, Wang H, Zhang Y, Jiang J, Tang J. Progressive global perception and local polishing network for lung infection segmentation of COVID-19 CT images. Pattern Recognit 2021;120:108168. [PMID: 34305181 DOI: 10.1016/j.patcog.2021.108168] [Cited by in Crossref: 24] [Cited by in F6Publishing: 20] [Article Influence: 12.0] [Reference Citation Analysis]
409 Soleymani Y, Jahanshahi AR, Hefzi M, Fazel Ghaziani M, Pourfarshid A, Khezerloo D. Evaluation of textural-based radiomics features for differentiation of COVID-19 pneumonia from non-COVID pneumonia. Egypt J Radiol Nucl Med 2021;52:219. [DOI: 10.1186/s43055-021-00592-0] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
410 Pouresmaieli M, Ekrami E, Akbari A, Noorbakhsh N, Moghadam NB, Mamoudifard M. A comprehensive review on efficient approaches for combating coronaviruses. Biomed Pharmacother 2021;144:112353. [PMID: 34794240 DOI: 10.1016/j.biopha.2021.112353] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
411 Duong LT, Le NH, Tran TB, Ngo VM, Nguyen PT. Detection of tuberculosis from chest X-ray images: Boosting the performance with vision transformer and transfer learning. Expert Systems with Applications 2021;184:115519. [DOI: 10.1016/j.eswa.2021.115519] [Cited by in Crossref: 18] [Cited by in F6Publishing: 11] [Article Influence: 9.0] [Reference Citation Analysis]
412 Halder A, Datta B. COVID-19 detection from lung CT-scan images using transfer learning approach. Mach Learn : Sci Technol 2021;2:045013. [DOI: 10.1088/2632-2153/abf22c] [Cited by in Crossref: 9] [Cited by in F6Publishing: 12] [Article Influence: 4.5] [Reference Citation Analysis]
413 Huang Z, Lei H, Chen G, Li H, Li C, Gao W, Chen Y, Wang Y, Xu H, Ma G, Lei B. Multi-center sparse learning and decision fusion for automatic COVID-19 diagnosis. Appl Soft Comput 2022;115:108088. [PMID: 34840541 DOI: 10.1016/j.asoc.2021.108088] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
414 Mohan N, Kabeer S, Nasir N. Artificial Intelligence (AI) in the diagnosis of COVID-19 Detection: A Review. 2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) 2021. [DOI: 10.1109/icecs53924.2021.9665470] [Reference Citation Analysis]
415 Qian H, Dong B, Yuan JJ, Yin F, Wang Z, Wang HN, Wang HS, Tian D, Li WH, Zhang B, Zhao LB, Ning BT. Pre-Consultation System Based on the Artificial Intelligence Has a Better Diagnostic Performance Than the Physicians in the Outpatient Department of Pediatrics. Front Med (Lausanne) 2021;8:695185. [PMID: 34820391 DOI: 10.3389/fmed.2021.695185] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
416 Abiyev RH, Ismail A, Ahmadian A. COVID-19 and Pneumonia Diagnosis in X-Ray Images Using Convolutional Neural Networks. Mathematical Problems in Engineering 2021;2021:1-14. [DOI: 10.1155/2021/3281135] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
417 Naseer A, Tamoor M, Azhar A. Computer-aided COVID-19 diagnosis and a comparison of deep learners using augmented CXRs. J Xray Sci Technol 2021. [PMID: 34842222 DOI: 10.3233/XST-211047] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
418 Rafik HD. Classification and detection of covid-19 in human respiratory lungs using convolutional neural network architectures. 2021 International Conference on Artificial Intelligence for Cyber Security Systems and Privacy (AI-CSP) 2021. [DOI: 10.1109/ai-csp52968.2021.9671158] [Reference Citation Analysis]
419 Chirila L, Cristea D, Banias O. CXR-based Diagnosis of COVID-19 using Deep Learning with CycleGAN for Data Augmentation. 2021 International Conference on e-Health and Bioengineering (EHB) 2021. [DOI: 10.1109/ehb52898.2021.9657539] [Reference Citation Analysis]
420 Bai X, Wang H, Ma L, Xu Y, Gan J, Fan Z, Yang F, Ma K, Yang J, Bai S, Shu C, Zou X, Huang R, Zhang C, Liu X, Tu D, Xu C, Zhang W, Wang X, Chen A, Zeng Y, Yang D, Wang MW, Holalkere N, Halin NJ, Kamel IR, Wu J, Peng X, Wang X, Shao J, Mongkolwat P, Zhang J, Liu W, Roberts M, Teng Z, Beer L, Sanchez LE, Sala E, Rubin D, Weller A, Lasenby J, Zheng C, Wang J, Li Z, Schönlieb CB, Xia T. Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence. ArXiv 2021:arXiv. [PMID: 34815983] [Reference Citation Analysis]
421 Wang SH, Zhu Z, Zhang YD. PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis. Front Public Health 2021;9:768278. [PMID: 34778194 DOI: 10.3389/fpubh.2021.768278] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
422 Hendrik B, Fauziah F. Implementation of Deep Learning Using Matlab-Based Convolutional Neural Network for Covid-19 Forecasting and Classification. 2021 International Conference on Computer Science and Engineering (IC2SE) 2021. [DOI: 10.1109/ic2se52832.2021.9792082] [Reference Citation Analysis]
423 Feng Y, Liu S, Cheng Z, Quiroz JC, Rezazadegan D, Chen P, Lin Q, Qian L, Liu X, Berkovsky S, Coiera E, Song L, Qiu X, Cai X. Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT. Information 2021;12:471. [DOI: 10.3390/info12110471] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
424 Li R, Li L, Xu Y, Yang J. Machine learning meets omics: applications and perspectives. Brief Bioinform 2021:bbab460. [PMID: 34791021 DOI: 10.1093/bib/bbab460] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
425 Thurzo A, Kosnáčová HS, Kurilová V, Kosmeľ S, Beňuš R, Moravanský N, Kováč P, Kuracinová KM, Palkovič M, Varga I. Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy. Healthcare (Basel) 2021;9:1545. [PMID: 34828590 DOI: 10.3390/healthcare9111545] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 5.5] [Reference Citation Analysis]
426 Fernandez-grandon C, Soto I, Zabala-blanco D, Alavia W, Garcia V. SVM and ANN classification using GLCM and HOG features for COVID-19 and Pneumonia detection from Chest X-rays. 2021 Third South American Colloquium on Visible Light Communications (SACVLC) 2021. [DOI: 10.1109/sacvlc53127.2021.9652248] [Reference Citation Analysis]
427 Kaur A, Chopra M, Bhushan M, Gupta S, Kumari P H, Sivagurunathan N, Shukla N, Rajagopal S, Bhalothia P, Sharma P, Naravula J, Suravajhala R, Gupta A, Abbasi BA, Goswami P, Singh H, Narang R, Polavarapu R, Medicherla KM, Valadi J, Kumar S A, Chaubey G, Singh KK, Bandapalli OR, Kavi Kishor PB, Suravajhala P. The Omic Insights on Unfolding Saga of COVID-19. Front Immunol 2021;12:724914. [PMID: 34745097 DOI: 10.3389/fimmu.2021.724914] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
428 Chen YM, Chen YJ, Ho WH, Tsai JT. Classifying chest CT images as COVID-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method. BMC Bioinformatics 2021;22:147. [PMID: 34749629 DOI: 10.1186/s12859-021-04083-x] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
429 Teodoro AAM, Silva DH, Saadi M, Okey OD, Rosa RL, Otaibi SA, Rodríguez DZ. An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19. J Signal Process Syst 2021;95:1-13. [PMID: 34777680 DOI: 10.1007/s11265-021-01714-7] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
430 Abdel-Basset M, Hawash H, Moustafa N, Elkomy OM. Two-Stage Deep Learning Framework for Discrimination between COVID-19 and Community-Acquired Pneumonia from Chest CT scans. Pattern Recognit Lett 2021;152:311-9. [PMID: 34728870 DOI: 10.1016/j.patrec.2021.10.027] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
431 Arntfield R, Wu D, Tschirhart J, VanBerlo B, Ford A, Ho J, McCauley J, Wu B, Deglint J, Chaudhary R, Dave C, VanBerlo B, Basmaji J, Millington S. Automation of Lung Ultrasound Interpretation via Deep Learning for the Classification of Normal versus Abnormal Lung Parenchyma: A Multicenter Study. Diagnostics (Basel) 2021;11:2049. [PMID: 34829396 DOI: 10.3390/diagnostics11112049] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
432 Murugan R, Goel T, Mirjalili S, Chakrabartty DK. WOANet: Whale optimized deep neural network for the classification of COVID-19 from radiography images. Biocybern Biomed Eng 2021;41:1702-18. [PMID: 34720309 DOI: 10.1016/j.bbe.2021.10.004] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
433 Szabó IV, Simon J, Nardocci C, Kardos AS, Nagy N, Abdelrahman RH, Zsarnóczay E, Fejér B, Futácsi B, Müller V, Merkely B, Maurovich-Horvat P. The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia. Tomography 2021;7:697-710. [PMID: 34842822 DOI: 10.3390/tomography7040058] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
434 Kundu R, Singh PK, Mirjalili S, Sarkar R. COVID-19 detection from lung CT-Scans using a fuzzy integral-based CNN ensemble. Comput Biol Med 2021;138:104895. [PMID: 34649147 DOI: 10.1016/j.compbiomed.2021.104895] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 7.5] [Reference Citation Analysis]
435 Drakopoulos K, Randhawa RS. Why Perfect Tests May Not Be Worth Waiting For: Information as a Commodity. Management Science 2021;67:6678-93. [DOI: 10.1287/mnsc.2021.4029] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
436 Hajij M, Zamzmi G, Batayneh F. TDA-Net: Fusion of Persistent Homology and Deep Learning Features for COVID-19 Detection From Chest X-Ray Images. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2021. [DOI: 10.1109/embc46164.2021.9629828] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]
437 Qian K, Schmitt M, Zheng H, Koike T, Han J, Liu J, Ji W, Duan J, Song M, Yang Z, Ren Z, Liu S, Zhang Z, Yamamoto Y, Schuller BW. Computer Audition for Fighting the SARS-CoV-2 Corona Crisis-Introducing the Multitask Speech Corpus for COVID-19. IEEE Internet Things J 2021;8:16035-46. [PMID: 35782182 DOI: 10.1109/JIOT.2021.3067605] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 3.0] [Reference Citation Analysis]
438 Zhang B. A Comprehensive Review of Deep Learning-Based COVID-19 Detection Mechanisms Using CT Images. 2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) 2021. [DOI: 10.1109/conf-spml54095.2021.00029] [Reference Citation Analysis]
439 Komolafe TE, Cao Y, Nguchu BA, Monkam P, Olaniyi EO, Sun H, Zheng J, Yang X. Diagnostic Test Accuracy of Deep Learning Detection of COVID-19: A Systematic Review and Meta-Analysis. Acad Radiol 2021;28:1507-23. [PMID: 34649779 DOI: 10.1016/j.acra.2021.08.008] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
440 Zeng L, Zheng Z, Zhang R. Pneumonia X-ray Imaging Classification Based on an Interpretable Machine Learning Model. 2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) 2021. [DOI: 10.1109/conf-spml54095.2021.00067] [Reference Citation Analysis]
441 de Carvalho Brito V, Dos Santos PRS, de Sales Carvalho NR, de Carvalho Filho AO. COVID-index: A texture-based approach to classifying lung lesions based on CT images. Pattern Recognit 2021;119:108083. [PMID: 34121775 DOI: 10.1016/j.patcog.2021.108083] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
442 Watkinson N, Givargis T, Joe V, Nicolau A, Veidenbaum A. Detecting COVID-19 Related Pneumonia On CT Scans Using Hyperdimensional Computing. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2021. [DOI: 10.1109/embc46164.2021.9630898] [Reference Citation Analysis]
443 Altaf F, Islam SM, Akhtar N. Resetting the baseline: CT-based COVID-19 diagnosis with Deep Transfer Learning is not as accurate as widely thought. 2021 Digital Image Computing: Techniques and Applications (DICTA) 2021. [DOI: 10.1109/dicta52665.2021.9647158] [Reference Citation Analysis]
444 Fricks RB, Ria F, Chalian H, Khoshpouri P, Abadi E, Bianchi L, Segars WP, Samei E. Deep learning classification of COVID-19 in chest radiographs: performance and influence of supplemental training. J Med Imaging (Bellingham) 2021;8:064501. [PMID: 34869785 DOI: 10.1117/1.JMI.8.6.064501] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
445 Robson JF, Denholm SJ, Coffey M. Automated Processing and Phenotype Extraction of Ovine Medical Images Using a Combined Generative Adversarial Network and Computer Vision Pipeline. Sensors (Basel) 2021;21:7268. [PMID: 34770574 DOI: 10.3390/s21217268] [Reference Citation Analysis]
446 Alizadehsani R, Sharifrazi D, Izadi NH, Joloudari JH, Shoeibi A, Gorriz JM, Hussain S, Arco JE, Sani ZA, Khozeimeh F, Khosravi A, Nahavandi S, Islam SMS, Acharya UR. Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data. ACM Trans Multimedia Comput Commun Appl 2021;17:1-24. [DOI: 10.1145/3462635] [Cited by in Crossref: 15] [Cited by in F6Publishing: 7] [Article Influence: 7.5] [Reference Citation Analysis]
447 Mikkili I, Karlapudi AP, Venkateswarulu TC, Kodali VP, Macamdas DSS, Sreerama K. Potential of artificial intelligence to accelerate diagnosis and drug discovery for COVID-19. PeerJ 2021;9:e12073. [PMID: 34707924 DOI: 10.7717/peerj.12073] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
448 Granata V, Ianniello S, Fusco R, Urraro F, Pupo D, Magliocchetti S, Albarello F, Campioni P, Cristofaro M, Di Stefano F, Fusco N, Petrone A, Schininà V, Villanacci A, Grassi F, Grassi R, Grassi R. Quantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software. J Pers Med 2021;11:1103. [PMID: 34834455 DOI: 10.3390/jpm11111103] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
449 Sufian MM, Moung EG, Hou CJ, Farzamnia A. Deep Learning Feature Extraction for COVID19 Detection Algorithm using Computerized Tomography Scan. 2021 11th International Conference on Computer Engineering and Knowledge (ICCKE) 2021. [DOI: 10.1109/iccke54056.2021.9721469] [Reference Citation Analysis]
450 Tu H, Zhao H, Su J, Wu W, Xu K, Hu J, Zhang X, Yang M, Sheng J. Predictors of COVID-19 Infection: A Prevalence Study of Hospitalized Patients. Can J Infect Dis Med Microbiol 2021;2021:6213450. [PMID: 34691316 DOI: 10.1155/2021/6213450] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
451 Amirian M, Montoya-zegarra JA, Gruss J, Stebler YD, Bozkir AS, Calandri M, Schwenker F, Stadelmann T. PrepNet: A Convolutional Auto-Encoder to Homogenize CT Scans for Cross-Dataset Medical Image Analysis. 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) 2021. [DOI: 10.1109/cisp-bmei53629.2021.9624344] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
452 Zhu L, Qu X, Wei S. Deep learning-based context aggregation network for tumor diagnosis. 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) 2021. [DOI: 10.1109/cisp-bmei53629.2021.9624424] [Reference Citation Analysis]
453 Al-jumaili S, Al-azzawi A, Duru AD, Ibrahim AA. Covid-19 X-ray image classification using SVM based on Local Binary Pattern. 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) 2021. [DOI: 10.1109/ismsit52890.2021.9604731] [Reference Citation Analysis]
454 Huynh HN, Diep QTN, Pham MB, Tran AT, Tran TN. Analysis and detection of COVID-19 cases on chest X-ray images using a novel architecture self-development deep-learning. 2021 IEEE International Biomedical Instrumentation and Technology Conference (IBITeC) 2021. [DOI: 10.1109/ibitec53045.2021.9649263] [Reference Citation Analysis]
455 Wang L, Zhang Y, Wang D, Tong X, Liu T, Zhang S, Huang J, Zhang L, Chen L, Fan H, Clarke M. Artificial Intelligence for COVID-19: A Systematic Review. Front Med (Lausanne) 2021;8:704256. [PMID: 34660623 DOI: 10.3389/fmed.2021.704256] [Cited by in Crossref: 22] [Cited by in F6Publishing: 23] [Article Influence: 11.0] [Reference Citation Analysis]
456 Wang T, Chen Z, Shang Q, Ma C, Chen X, Xiao E. A Promising and Challenging Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19. Diagnostics (Basel) 2021;11:1924. [PMID: 34679622 DOI: 10.3390/diagnostics11101924] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
457 Kart Ö, Basciftci F. Makine Öğrenmesi Algoritmalarıyla Akciğer Tomografi Görüntülerinden COVID-19 Tespiti. European Journal of Science and Technology 2021. [DOI: 10.31590/ejosat.1009611] [Reference Citation Analysis]
458 Yu L, Shi X, Liu X, Jin W, Jia X, Xi S, Wang A, Li T, Zhang X, Tian G, Sun D. Artificial Intelligence Systems for Diagnosis and Clinical Classification of COVID-19. Front Microbiol 2021;12:729455. [PMID: 34650534 DOI: 10.3389/fmicb.2021.729455] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
459 Khaloufi H, Abouelmehdi K, Beni-Hssane A, Rustam F, Jurcut AD, Lee E, Ashraf I. Deep Learning Based Early Detection Framework for Preliminary Diagnosis of COVID-19 via Onboard Smartphone Sensors. Sensors (Basel) 2021;21:6853. [PMID: 34696066 DOI: 10.3390/s21206853] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
460 Sarker S, Jamal L, Ahmed SF, Irtisam N. Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review. Rob Auton Syst 2021;146:103902. [PMID: 34629751 DOI: 10.1016/j.robot.2021.103902] [Cited by in Crossref: 13] [Cited by in F6Publishing: 4] [Article Influence: 6.5] [Reference Citation Analysis]
461 Al-Dorzi HM, Aldawood AS, Almatrood A, Burrows V, Naidu B, Alchin JD, Alhumedi H, Tashkandi N, Al-Jahdali H, Hussain A, Al Harbi MK, Al Zaibag M, Bin Salih S, Al Shamrani MM, Alsaawi A, Arabi YM. Managing critical care during COVID-19 pandemic: The experience of an ICU of a tertiary care hospital. J Infect Public Health 2021;14:1635-41. [PMID: 34627058 DOI: 10.1016/j.jiph.2021.09.018] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]
462 Kim EY, Chung MJ. Application of artificial intelligence in chest imaging for COVID-19. J Korean Med Assoc 2021;64:664-70. [DOI: 10.5124/jkma.2021.64.10.664] [Reference Citation Analysis]
463 Nguyen HT, Bao Tran T, Luong HH, Nguyen Huynh TK. Decoders configurations based on Unet family and feature pyramid network for COVID-19 segmentation on CT images. PeerJ Comput Sci 2021;7:e719. [PMID: 34616895 DOI: 10.7717/peerj-cs.719] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
464 Ruzicka M, Volosin M, Gazda J, Maksymyuk T. Deep Learning-Based Blockchain Framework for the COVID-19 Spread Monitoring. 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) 2021. [DOI: 10.1109/iceccme52200.2021.9591057] [Reference Citation Analysis]
465 Saad Y, Mustapha A, Cherry A. Automatic classification between COVID-19 pneumonia, lung cancer and normal lung tissues on chest CT Scans. 2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME) 2021. [DOI: 10.1109/icabme53305.2021.9604860] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
466 Olulana K, Owolawi P, Tu C, Abe B. Long Thorax Disease Classification Using Convolutional Long Short Term Memory. 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) 2021. [DOI: 10.1109/iceccme52200.2021.9591041] [Reference Citation Analysis]
467 Wang XH, Xu X, Ao Z, Duan J, Han X, Tang X, Fu YF, Wu XS, Wang X, Zhu L, Zeng W, Guo S. Elaboration of a Radiomics Strategy for the Prediction of the Re-positive Cases in the Discharged Patients With COVID-19. Front Med (Lausanne) 2021;8:730441. [PMID: 34604267 DOI: 10.3389/fmed.2021.730441] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
468 Vahedian-Azimi A, Keramatfar A, Asiaee M, Atashi SS, Nourbakhsh M. Do you have COVID-19? An artificial intelligence-based screening tool for COVID-19 using acoustic parameters. J Acoust Soc Am 2021;150:1945. [PMID: 34598596 DOI: 10.1121/10.0006104] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
469 Gil D, Baeza S, Sanchez C, Torres G, Garcia-olive I, Moragas G, Deportos J, Salcedo M, Rosell A. Intelligent Radiomic Analysis of Q-SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients. 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) 2021. [DOI: 10.1109/iccvw54120.2021.00054] [Reference Citation Analysis]
470 [DOI: 10.1109/iccvw54120.2021.00063] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
471 Aminu M, Ahmad NA, Mohd Noor MH. Covid-19 detection via deep neural network and occlusion sensitivity maps. Alexandria Engineering Journal 2021;60:4829-55. [DOI: 10.1016/j.aej.2021.03.052] [Cited by in Crossref: 15] [Cited by in F6Publishing: 6] [Article Influence: 7.5] [Reference Citation Analysis]
472 Stokes K, Castaldo R, Franzese M, Salvatore M, Fico G, Pokvic LG, Badnjevic A, Pecchia L. A machine learning model for supporting symptom-based referral and diagnosis of bronchitis and pneumonia in limited resource settings. Biocybernetics and Biomedical Engineering 2021;41:1288-302. [DOI: 10.1016/j.bbe.2021.09.002] [Cited by in Crossref: 14] [Cited by in F6Publishing: 4] [Article Influence: 7.0] [Reference Citation Analysis]
473 Tan W, Liu J. A 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From CT-Scan Images. 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) 2021. [DOI: 10.1109/iccvw54120.2021.00053] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
474 Kavuran G, İn E, Geçkil AA, Şahin M, Berber NK. MTU-COVNet: A hybrid methodology for diagnosing the COVID-19 pneumonia with optimized features from multi-net. Clin Imaging 2022;81:1-8. [PMID: 34592696 DOI: 10.1016/j.clinimag.2021.09.007] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
475 Hou J, Xu J, Feng R, Zhang Y, Shan F, Shi W. CMC-COV19D: Contrastive Mixup Classification for COVID-19 Diagnosis. 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) 2021. [DOI: 10.1109/iccvw54120.2021.00055] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
476 Fusco R, Grassi R, Granata V, Setola SV, Grassi F, Cozzi D, Pecori B, Izzo F, Petrillo A. Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment. J Pers Med 2021;11:993. [PMID: 34683133 DOI: 10.3390/jpm11100993] [Cited by in Crossref: 17] [Cited by in F6Publishing: 19] [Article Influence: 8.5] [Reference Citation Analysis]
477 Ardali Duzgun S, Durhan G, Basaran Demirkazik F, Irmak I, Karakaya J, Akpinar E, Gulsun Akpinar M, Inkaya AC, Ocal S, Topeli A, Ariyurek OM. AI-Based Quantitative CT Analysis of Temporal Changes According to Disease Severity in COVID-19 Pneumonia. J Comput Assist Tomogr 2021;45:970-8. [PMID: 34581706 DOI: 10.1097/RCT.0000000000001224] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
478 Bazel MA, Mohammed F, Alsabaiy M, Abualrejal HM. The role of Internet of Things, Blockchain, Artificial Intelligence, and Big Data Technologies in Healthcare to Prevent the Spread of the COVID-19. 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) 2021. [DOI: 10.1109/3ict53449.2021.9581469] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
479 Lahsaini I, El Habib Daho M, Chikh MA. Deep transfer learning based classification model for covid-19 using chest CT-scans. Pattern Recognit Lett 2021;152:122-8. [PMID: 34566222 DOI: 10.1016/j.patrec.2021.08.035] [Cited by in Crossref: 6] [Cited by in F6Publishing: 11] [Article Influence: 3.0] [Reference Citation Analysis]
480 Shetty AA, Hegde NT, Vaz AC, Srinivasan CR. Deep Learning Methodologies for Diagnosis of Respiratory Disorders from Chest X-ray Images: A Comparative Study. IOCA 2021 2021. [DOI: 10.3390/ioca2021-10900] [Reference Citation Analysis]
481 Bedrikovetski S, Dudi-Venkata NN, Kroon HM, Seow W, Vather R, Carneiro G, Moore JW, Sammour T. Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis. BMC Cancer 2021;21:1058. [PMID: 34565338 DOI: 10.1186/s12885-021-08773-w] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
482 [DOI: 10.1109/eiecs53707.2021.9587953] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
483 Alharbi A, Abdur Rahman MD. Review of Recent Technologies for Tackling COVID-19. SN Comput Sci 2021;2:460. [PMID: 34549196 DOI: 10.1007/s42979-021-00841-z] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
484 Lorenzen SS, Nielsen M, Jimenez-Solem E, Petersen TS, Perner A, Thorsen-Meyer HC, Igel C, Sillesen M. Using machine learning for predicting intensive care unit resource use during the COVID-19 pandemic in Denmark. Sci Rep 2021;11:18959. [PMID: 34556789 DOI: 10.1038/s41598-021-98617-1] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
485 Juneja S, Juneja A, Bali V, Jain V. COVID‐19 and Machine Learning Approaches to Deal With the Pandemic. Enabling Healthcare 4.0 for Pandemics 2021. [DOI: 10.1002/9781119769088.ch1] [Reference Citation Analysis]
486 Afshar P, Heidarian S, Naderkhani F, Rafiee MJ, Oikonomou A, Plataniotis KN, Mohammadi A. Hybrid Deep Learning Model For Diagnosis Of Covid-19 Using Ct Scans And Clinical/Demographic Data. 2021 IEEE International Conference on Image Processing (ICIP) 2021. [DOI: 10.1109/icip42928.2021.9506661] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
487 Cao J, Jiang L, Hou J, Jiang L, Zhao R, Shi W, Shan F, Feng R. Exploiting Deep Cross-Slice Features From CT Images For Multi-Class Pneumonia Classification. 2021 IEEE International Conference on Image Processing (ICIP) 2021. [DOI: 10.1109/icip42928.2021.9506553] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
488 Qi S, Xu C, Li C, Tian B, Xia S, Ren J, Yang L, Wang H, Yu H. DR-MIL: deep represented multiple instance learning distinguishes COVID-19 from community-acquired pneumonia in CT images. Comput Methods Programs Biomed 2021;211:106406. [PMID: 34536634 DOI: 10.1016/j.cmpb.2021.106406] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
489 Li Q, Ning J, Yuan J, Xiao L. A depthwise separable dense convolutional network with convolution block attention module for COVID-19 diagnosis on CT scans. Comput Biol Med 2021;137:104837. [PMID: 34530335 DOI: 10.1016/j.compbiomed.2021.104837] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
490 Fu Y, Xue P, Dong E. Densely connected attention network for diagnosing COVID-19 based on chest CT. Comput Biol Med 2021;137:104857. [PMID: 34520988 DOI: 10.1016/j.compbiomed.2021.104857] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
491 Ding X, Xu J, Xu H, Zhou J, Long Q. Risk Assessment Using Early Quantitative Chest CT Parameters for the Severity of COVID-19. Iran J Radiol 2021;18. [DOI: 10.5812/iranjradiol.109439] [Reference Citation Analysis]
492 Feng DY, Ren Y, Zhou M, Zou XL, Wu WB, Yang HL, Zhou YQ, Zhang TT. Deep Learning-Based Available and Common Clinical-Related Feature Variables Robustly Predict Survival in Community-Acquired Pneumonia. Risk Manag Healthc Policy 2021;14:3701-9. [PMID: 34512057 DOI: 10.2147/RMHP.S317735] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
493 K M P, Sharan S R, M N, M R. Praxis of Technology and Tools in COVID-19 Response. RJPT 2021. [DOI: 10.52711/0974-360x.2021.00836] [Reference Citation Analysis]
494 Carmo D, Campiotti I, Rodrigues L, Fantini I, Pinheiro G, Moraes D, Nogueira R, Rittner L, Lotufo R. Rapidly deploying a COVID-19 decision support system in one of the largest Brazilian hospitals. Health Informatics J 2021;27:14604582211033017. [PMID: 34510949 DOI: 10.1177/14604582211033017] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
495 Alirr OI. Automatic deep learning system for COVID-19 infection quantification in chest CT. Multimed Tools Appl 2021;:1-15. [PMID: 34539221 DOI: 10.1007/s11042-021-11299-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
496 Wu T, Tang C, Xu M, Hong N, Lei Z. ULNet for the detection of coronavirus (COVID-19) from chest X-ray images. Comput Biol Med 2021;137:104834. [PMID: 34507159 DOI: 10.1016/j.compbiomed.2021.104834] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
497 Shankar K, Mohanty SN, Yadav K, Gopalakrishnan T, Elmisery AM. Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model. Cogn Neurodyn 2021;:1-14. [PMID: 34522236 DOI: 10.1007/s11571-021-09712-y] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
498 Zhang F. Application of machine learning in CT images and X-rays of COVID-19 pneumonia. Medicine (Baltimore) 2021;100:e26855. [PMID: 34516488 DOI: 10.1097/MD.0000000000026855] [Cited by in Crossref: 18] [Cited by in F6Publishing: 13] [Article Influence: 9.0] [Reference Citation Analysis]
499 Hong G, Chen X, Chen J, Zhang M, Ren Y, Zhang X. A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19. Sci Rep 2021;11:18048. [PMID: 34508120 DOI: 10.1038/s41598-021-97428-8] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
500 Sanket S, Vergin Raja Sarobin M, Jani Anbarasi L, Thakor J, Singh U, Narayanan S. Detection of novel coronavirus from chest X-rays using deep convolutional neural networks. Multimed Tools Appl 2021;:1-26. [PMID: 34512112 DOI: 10.1007/s11042-021-11257-5] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
501 Chen HJ, Mao L, Chen Y, Yuan L, Wang F, Li X, Cai Q, Qiu J, Chen F. Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia. BMC Infect Dis 2021;21:931. [PMID: 34496794 DOI: 10.1186/s12879-021-06614-6] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
502 Reeves JJ, Pageler NM, Wick EC, Melton GB, Tan YG, Clay BJ, Longhurst CA. The Clinical Information Systems Response to the COVID-19 Pandemic. Yearb Med Inform 2021;30:105-25. [PMID: 34479384 DOI: 10.1055/s-0041-1726513] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
503 Rao K, Xie K, Hu Z, Guo X, Wen C, He J. COVID-19 detection method based on SVRNet and SVDNet in lung x-rays. J Med Imaging (Bellingham) 2021;8:017504. [PMID: 34471647 DOI: 10.1117/1.JMI.8.S1.017504] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
504 Onishi T, Honda N, Igarashi Y. Optimal and worst testing strategies for COVID-19.. [DOI: 10.1101/2021.08.31.21262868] [Reference Citation Analysis]
505 Mahmud T, Alam MJ, Chowdhury S, Ali SN, Rahman MM, Anowarul Fattah S, Saquib M. CovTANet: A Hybrid Tri-Level Attention-Based Network for Lesion Segmentation, Diagnosis, and Severity Prediction of COVID-19 Chest CT Scans. IEEE Trans Ind Inf 2021;17:6489-98. [DOI: 10.1109/tii.2020.3048391] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
506 Liu F, Tang J, Ma J, Wang C, Ha Q, Yu Y, Zhou Z. The application of artificial intelligence to chest medical image analysis. Intelligent Medicine 2021;1:104-117. [DOI: 10.1016/j.imed.2021.06.004] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
507 Liz H, Sánchez-montañés M, Tagarro A, Domínguez-rodríguez S, Dagan R, Camacho D. Ensembles of Convolutional Neural Network models for pediatric pneumonia diagnosis. Future Generation Computer Systems 2021;122:220-33. [DOI: 10.1016/j.future.2021.04.007] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
508 Song X, Li H, Gao W, Chen Y, Wang T, Ma G, Lei B. Augmented Multicenter Graph Convolutional Network for COVID-19 Diagnosis. IEEE Trans Ind Inf 2021;17:6499-509. [DOI: 10.1109/tii.2021.3056686] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
509 Liu C, Yin Q. Automatic Diagnosis of COVID-19 Using a tailored Transformer-Like Network. J Phys : Conf Ser 2021;2010:012175. [DOI: 10.1088/1742-6596/2010/1/012175] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
510 Monti R, Pagliei V, Palumbo P, Bruno F, Arrigoni F, Di Cesare E, Splendiani A, Barile A, Masciocchi C. Spectrum of radiographic findings of new Coronavirus disease 2019. G Ital Radiol Med 2021;8. [DOI: 10.23736/s2723-9284.21.00152-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
511 Ji D, Zhang Z, Zhao Y, Zhao Q. Research on Classification of COVID-19 Chest X-Ray Image Modal Feature Fusion Based on Deep Learning. J Healthc Eng 2021;2021:6799202. [PMID: 34457220 DOI: 10.1155/2021/6799202] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
512 Lipták P, Banovčin P, Rosoľanka R, Prokopič M, Kocan I, Žiačikova I, Uhrík P, Grendár M, Hyrdel R. A machine learning approach for identification of gastrointestinal predictors for the risk of COVID-19 related hospitalization.. [DOI: 10.1101/2021.08.27.21262728] [Reference Citation Analysis]
513 Akbari Y, Hassen H, Al-maadeed S, Zughaier SM. COVID-19 Lesion Segmentation Using Lung CT Scan Images: Comparative Study Based on Active Contour Models. Applied Sciences 2021;11:8039. [DOI: 10.3390/app11178039] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
514 Wang SH, Satapathy SC, Anderson D, Chen SX, Zhang YD. Deep Fractional Max Pooling Neural Network for COVID-19 Recognition. Front Public Health 2021;9:726144. [PMID: 34447739 DOI: 10.3389/fpubh.2021.726144] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
515 Liu M, Lv W, Yin B, Ge Y, Wei W. The human-AI scoring system: A new method for CT-based assessment of COVID-19 severity. Technol Health Care 2021. [PMID: 34486996 DOI: 10.3233/THC-213199] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
516 Delli Pizzi A, Chiarelli AM, Chiacchiaretta P, Valdesi C, Croce P, Mastrodicasa D, Villani M, Trebeschi S, Serafini FL, Rosa C, Cocco G, Luberti R, Conte S, Mazzamurro L, Mereu M, Patea RL, Panara V, Marinari S, Vecchiet J, Caulo M. Radiomics-based machine learning differentiates "ground-glass" opacities due to COVID-19 from acute non-COVID-19 lung disease. Sci Rep 2021;11:17237. [PMID: 34446812 DOI: 10.1038/s41598-021-96755-0] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
517 [DOI: 10.1109/inista52262.2021.9548642] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
518 Ahsan MM, Nazim R, Siddique Z, Huebner P. Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME. Healthcare (Basel) 2021;9:1099. [PMID: 34574873 DOI: 10.3390/healthcare9091099] [Cited by in Crossref: 16] [Cited by in F6Publishing: 19] [Article Influence: 8.0] [Reference Citation Analysis]
519 Serte S, Serener A. Classification of COVID-19 and pleural effusion on chest radiographs using CNN fusion. 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) 2021. [DOI: 10.1109/inista52262.2021.9548502] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
520 Hariri W, Narin A. Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review. Soft comput 2021;:1-18. [PMID: 34456618 DOI: 10.1007/s00500-021-06137-x] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
521 Xiao B, Yang Z, Qiu X, Xiao J, Wang G, Zeng W, Li W, Nian Y, Chen W. PAM-DenseNet: A Deep Convolutional Neural Network for Computer-Aided COVID-19 Diagnosis. IEEE Trans Cybern 2021;PP. [PMID: 34428169 DOI: 10.1109/TCYB.2020.3042837] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
522 Monday HN, Li JP, Nneji GU, James EC, Chikwendu IA, Ejiyi CJ, Oluwasanmi A, Mgbejime GT. The Capability of Multi Resolution Analysis: A Case Study of COVID-19 Diagnosis. 2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI) 2021. [DOI: 10.1109/prai53619.2021.9550802] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
523 Turchi GP, Dalla Riva MS, Ciloni C, Moro C, Orrù L. The Interactive Management of the SARS-CoV-2 Virus: The Social Cohesion Index, a Methodological-Operational Proposal. Front Psychol 2021;12:559842. [PMID: 34408687 DOI: 10.3389/fpsyg.2021.559842] [Cited by in Crossref: 3] [Cited by in F6Publishing: 6] [Article Influence: 1.5] [Reference Citation Analysis]
524 Monday HN, Li JP, Nneji GU, Oluwasanmi A, Mgbejime GT, Ejiyi CJ, Chikwendu IA, James EC. Improved Convolutional Neural Multi-Resolution Wavelet Network for COVID-19 Pneumonia Classification. 2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI) 2021. [DOI: 10.1109/prai53619.2021.9551095] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
525 Lu W, Wei J, Xu T, Ding M, Li X, He M, Chen K, Yang X, She H, Huang B. Quantitative CT for detecting COVID‑19 pneumonia in suspected cases. BMC Infect Dis 2021;21:836. [PMID: 34412614 DOI: 10.1186/s12879-021-06556-z] [Reference Citation Analysis]
526 Perumal V, Narayanan V, Rajasekar SJS. Prediction of COVID Criticality Score with Laboratory, Clinical and CT Images using Hybrid Regression Models. Comput Methods Programs Biomed 2021;209:106336. [PMID: 34403841 DOI: 10.1016/j.cmpb.2021.106336] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
527 Monday HN, Li JP, Nneji GU, Hossin MA, Kumar R, Oluwasanmi A, James EC, Mgbejime GT, Umana ES, Chikwendu IA, Ejiyi CJ, Ogungbile A, Dike ID, Ukwuoma CC. COVID-19 Diagnosis from Chest X-Ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super Resolution Convolutional Neural Network: Algorithm Development and Validation (Preprint).. [DOI: 10.2196/preprints.32913] [Reference Citation Analysis]
528 Nneji GU, Cai J, Jianhua D, Hossin MA, Monday HN, Oluwasanmi A, Chikwendu IA, Ejiyi CJ, Ukwuoma CC, James EC, Mgbejime GT. Fine-tuned Siamese Network with Modified Enhanced Super-Resolution GAN Plus Based on Low Quality Chest X-ray Images for COVID-19 Identification: Algorithm Development and Validation (Preprint).. [DOI: 10.2196/preprints.32915] [Reference Citation Analysis]
529 Sharafeldeen A, Elsharkawy M, Alghamdi NS, Soliman A, El-Baz A. Precise Segmentation of COVID-19 Infected Lung from CT Images Based on Adaptive First-Order Appearance Model with Morphological/Anatomical Constraints. Sensors (Basel) 2021;21:5482. [PMID: 34450923 DOI: 10.3390/s21165482] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
530 Rodríguez-Rodríguez I, Rodríguez JV, Shirvanizadeh N, Ortiz A, Pardo-Quiles DJ. Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining. Int J Environ Res Public Health 2021;18:8578. [PMID: 34444327 DOI: 10.3390/ijerph18168578] [Cited by in Crossref: 12] [Cited by in F6Publishing: 15] [Article Influence: 6.0] [Reference Citation Analysis]
531 Heidarian S, Afshar P, Enshaei N, Naderkhani F, Rafiee MJ, Oikonomou A, Fard FB, Shafiee A, Plataniotis KN, Mohammadi A. Wso-Caps: Diagnosis Of Lung Infection From Low And Ultra-Lowdose CT Scans Using Capsule Networks And Windowsetting Optimization. 2021 IEEE International Conference on Autonomous Systems (ICAS) 2021. [DOI: 10.1109/icas49788.2021.9551176] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
532 Chadaga K, Prabhu S, Vivekananda BK, Niranjana S, Umakanth S, Pham DT. Battling COVID-19 using machine learning: A review. Cogent Engineering 2021;8:1958666. [DOI: 10.1080/23311916.2021.1958666] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
533 Quah J, Liew CJY, Zou L, Koh XH, Alsuwaigh R, Narayan V, Lu TY, Ngoh C, Wang Z, Koh JZ, Ang C, Fu Z, Goh HL. Chest radiograph-based artificial intelligence predictive model for mortality in community-acquired pneumonia. BMJ Open Respir Res 2021;8:e001045. [PMID: 34376402 DOI: 10.1136/bmjresp-2021-001045] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
534 Goel G, Gondhalekar A, Qi J, Zhang Z, Cao G, Feng W. ComputeCOVID19+: Accelerating COVID-19 Diagnosis and Monitoring via High-Performance Deep Learning on CT Images. 50th International Conference on Parallel Processing 2021. [DOI: 10.1145/3472456.3473523] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
535 Kaheel H, Hussein A, Chehab A. AI-Based Image Processing for COVID-19 Detection in Chest CT Scan Images. Front Comms Net 2021;2:645040. [DOI: 10.3389/frcmn.2021.645040] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
536 Cheung YT, Zhang H, Cai J, Au-Doung LWP, Yang LS, Yan C, Zhou F, Chen X, Guan X, Pui CH, Hudson MM, Li CK. Identifying Priorities for Harmonizing Guidelines for the Long-Term Surveillance of Childhood Cancer Survivors in the Chinese Children Cancer Group (CCCG). JCO Glob Oncol 2021;7:261-76. [PMID: 33591820 DOI: 10.1200/GO.20.00534] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
537 Caruso D, Pucciarelli F, Zerunian M, Ganeshan B, De Santis D, Polici M, Rucci C, Polidori T, Guido G, Bracci B, Benvenga A, Barbato L, Laghi A. Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia. Radiol Med 2021. [PMID: 34347270 DOI: 10.1007/s11547-021-01402-3] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 3.5] [Reference Citation Analysis]
538 Yang Y, Li Q, Guo Y, Liu Y, Li X, Guo J, Li W, Cheng L, Chen H, Kang Y. Lung parenchyma parameters measure of rats from pulmonary window computed tomography images based on ResU-Net model for medical respiratory researches. Math Biosci Eng 2021;18:4193-211. [PMID: 34198432 DOI: 10.3934/mbe.2021210] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
539 Mader C, Bernatz S, Michalik S, Koch V, Martin SS, Mahmoudi S, Basten L, Grünewald LD, Bucher A, Albrecht MH, Vogl TJ, Booz C. Quantification of COVID-19 Opacities on Chest CT - Evaluation of a Fully Automatic AI-approach to Noninvasively Differentiate Critical Versus Noncritical Patients. Acad Radiol 2021;28:1048-57. [PMID: 33741210 DOI: 10.1016/j.acra.2021.03.001] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
540 Olveres J, González G, Torres F, Moreno-Tagle JC, Carbajal-Degante E, Valencia-Rodríguez A, Méndez-Sánchez N, Escalante-Ramírez B. What is new in computer vision and artificial intelligence in medical image analysis applications. Quant Imaging Med Surg 2021;11:3830-53. [PMID: 34341753 DOI: 10.21037/qims-20-1151] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
541 Arslan H, Arslan H. A new COVID-19 detection method from human genome sequences using CpG island features and KNN classifier. Engineering Science and Technology, an International Journal 2021;24:839-47. [DOI: 10.1016/j.jestch.2020.12.026] [Cited by in Crossref: 14] [Cited by in F6Publishing: 4] [Article Influence: 7.0] [Reference Citation Analysis]
542 Sengupta K, Srivastava PR. Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients. BMC Med Inform Decis Mak 2021;21:227. [PMID: 34330278 DOI: 10.1186/s12911-021-01588-6] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
543 Biswas S, Chatterjee S, Majee A, Sen S, Schwenker F, Sarkar R. Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models. Applied Sciences 2021;11:7004. [DOI: 10.3390/app11157004] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 8.0] [Reference Citation Analysis]
544 Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix AN, Lambin P, Bottari F, Tsoutzidis N, Miraglio B, Walsh S, Vos W, Hustinx R, Ferreira M, Lovinfosse P, Leijenaar RTH. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev 2021. [PMID: 34309893 DOI: 10.1002/med.21846] [Cited by in Crossref: 17] [Cited by in F6Publishing: 21] [Article Influence: 8.5] [Reference Citation Analysis]
545 Dong J, Wu H, Zhou D, Li K, Zhang Y, Ji H, Tong Z, Lou S, Liu Z. Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China. J Med Syst 2021;45:84. [PMID: 34302549 DOI: 10.1007/s10916-021-01757-0] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 3.5] [Reference Citation Analysis]
546 Laino ME, Ammirabile A, Posa A, Cancian P, Shalaby S, Savevski V, Neri E. The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review. Diagnostics (Basel) 2021;11:1317. [PMID: 34441252 DOI: 10.3390/diagnostics11081317] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
547 Bouchareb Y, Moradi Khaniabadi P, Al Kindi F, Al Dhuhli H, Shiri I, Zaidi H, Rahmim A. Artificial intelligence-driven assessment of radiological images for COVID-19. Comput Biol Med 2021;136:104665. [PMID: 34343890 DOI: 10.1016/j.compbiomed.2021.104665] [Cited by in Crossref: 30] [Cited by in F6Publishing: 32] [Article Influence: 15.0] [Reference Citation Analysis]
548 Pourhoseingholi A, Vahedi M, Chaibakhsh S, Pourhoseingholi MA, Vahedian-Azimi A, Guest PC, Rahimi-Bashar F, Sahebkar A. Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features. Adv Exp Med Biol 2021;1327:139-47. [PMID: 34279835 DOI: 10.1007/978-3-030-71697-4_11] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
549 Bhardwaj P, Kaur A. A novel and efficient deep learning approach for COVID-19 detection using X-ray imaging modality. Int J Imaging Syst Technol 2021. [PMID: 34518739 DOI: 10.1002/ima.22627] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
550 Bacellar GC, Chandrappa M, Kulkarni R, Dey S. COVID-19 Chest X-Ray Image Classification Using Deep Learning.. [DOI: 10.1101/2021.07.15.21260605] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
551 Madan S, Chaudhury S, Gandhi TK. Automated detection of COVID-19 on a small dataset of chest CT images using metric learning. 2021 International Joint Conference on Neural Networks (IJCNN) 2021. [DOI: 10.1109/ijcnn52387.2021.9533831] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
552 Catal Reis H. COVID-19 Diagnosis with Deep Learning. Ing Inv 2022;42:e88825. [DOI: 10.15446/ing.investig.v42n1.88825] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
553 Condaragiu S, Ciocoiu IB. Evaluation of Convolutional Neural Networks for COVID-19 Detection from Chest X-Ray Images. 2021 International Symposium on Signals, Circuits and Systems (ISSCS) 2021. [DOI: 10.1109/isscs52333.2021.9497418] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
554 Yousefi B, Kawakita S, Amini A, Akbari H, Advani SM, Akhloufi M, Maldague XPV, Ahadian S. Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics. J Clin Med 2021;10:3100. [PMID: 34300266 DOI: 10.3390/jcm10143100] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
555 Kamal Pasha M, Gardazi SFA, Imtiaz F, Qureshi AT, Afrasiab R. Identification of efficient COVID-19 diagnostic test through artificial neural networks approach − substantiated by modeling and simulation. Journal of Intelligent Systems 2020;30:836-54. [DOI: 10.1515/jisys-2021-0041] [Reference Citation Analysis]
556 Kara M, Öztürk Z, Akpek S, Turupcu A. COVID-19 Diagnosis from Chest CT Scans: A Weakly Supervised CNN-LSTM Approach. AI 2021;2:330-41. [DOI: 10.3390/ai2030020] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
557 Leite ML, de Loiola Costa LS, Cunha VA, Kreniski V, de Oliveira Braga Filho M, da Cunha NB, Costa FF. Artificial intelligence and the future of life sciences. Drug Discov Today 2021:S1359-6446(21)00308-1. [PMID: 34245910 DOI: 10.1016/j.drudis.2021.07.002] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
558 Grassi R, Cappabianca S, Urraro F, Granata V, Giacobbe G, Magliocchetti S, Cozzi D, Fusco R, Galdiero R, Picone C, Belfiore MP, Reginelli A, Atripaldi U, Picascia O, Coppola M, Bignardi E, Grassi R, Miele V. Evolution of CT Findings and Lung Residue in Patients with COVID-19 Pneumonia: Quantitative Analysis of the Disease with a Computer Automatic Tool. J Pers Med 2021;11:641. [PMID: 34357108 DOI: 10.3390/jpm11070641] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
559 Shen YT, Chen L, Yue WW, Xu HX. Digital Technology-Based Telemedicine for the COVID-19 Pandemic. Front Med (Lausanne) 2021;8:646506. [PMID: 34295908 DOI: 10.3389/fmed.2021.646506] [Cited by in Crossref: 11] [Cited by in F6Publishing: 13] [Article Influence: 5.5] [Reference Citation Analysis]
560 Monterde D, Carot-sans G, Cainzos-achirica M, Abilleira S, Coca M, Vela E, Clèries M, Valero D, Comin-colet J, Garcia-eroles L, Sust PP, Arrufat M, Lejardi Y, Piera J. Comorbidity accounts for severe COVID-19 risk, but how do we measure it? Retrospective assessment of the performance of three measures of comorbidity using 4,607 hospitalizations.. [DOI: 10.1101/2021.07.02.21259898] [Reference Citation Analysis]
561 Jingxin L, Mengchao Z, Yuchen L, Jinglei C, Yutong Z, Zhong Z, Lihui Z. COVID-19 lesion detection and segmentation-A deep learning method. Methods 2021:S1046-2023(21)00180-8. [PMID: 34237453 DOI: 10.1016/j.ymeth.2021.07.001] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
562 Dogan O, Tiwari S, Jabbar MA, Guggari S. A systematic review on AI/ML approaches against COVID-19 outbreak. Complex Intell Systems 2021;:1-24. [PMID: 34777970 DOI: 10.1007/s40747-021-00424-8] [Cited by in Crossref: 17] [Cited by in F6Publishing: 8] [Article Influence: 8.5] [Reference Citation Analysis]
563 Zhang Y, Zhang B. Hierarchical Automatic COVID-19 Detection via CT Scan Images. 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI) 2021. [DOI: 10.1109/bdai52447.2021.9515302] [Reference Citation Analysis]
564 Pezzutti DL, Wadhwa V, Makary MS. COVID-19 imaging: Diagnostic approaches, challenges, and evolving advances. WJR 2021;13:172-92. [DOI: 10.4329/wjr.v13.i6.172] [Reference Citation Analysis]
565 Vaidya T, Nanivadekar A, Patel R. Imaging spectrum of abdominal manifestations of COVID-19. World J Radiol 2021; 13(6): 157-170 [PMID: 34249237 DOI: 10.4329/wjr.v13.i6.157] [Cited by in CrossRef: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
566 Sideris GA, Nikolakea M, Karanikola AE, Konstantinopoulou S, Giannis D, Modahl L. Imaging in the COVID-19 era: Lessons learned during a pandemic. World J Radiol 2021; 13(6): 192-222 [PMID: 34249239 DOI: 10.4329/wjr.v13.i6.192] [Cited by in CrossRef: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
567 Sideris GA, Nikolakea M, Karanikola A, Konstantinopoulou S, Giannis D, Modahl L. Imaging in the COVID-19 era: Lessons learned during a pandemic. WJR 2021;13:193-223. [DOI: 10.4329/wjr.v13.i6.193] [Reference Citation Analysis]
568 Strutynskaya AD, Koshurnikov DS, Tyurin IE, Karnaushkina MA. Evaluation of an association of radiological findings and severity of the disease in patients with the new coronavirus infection (COVID-19). Alʹm klin med 2021;49:171-178. [DOI: 10.18786/2072-0505-2021-49-028] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
569 Wankhade M, Hore UW. Banana Ripeness Classification Based On Image Processing With Machine Learning. IJARSCT 2021. [DOI: 10.48175/ijarsct-1571] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
570 Pezzutti DL, Wadhwa V, Makary MS. COVID-19 imaging: Diagnostic approaches, challenges, and evolving advances. World J Radiol 2021; 13(6): 171-191 [PMID: 34249238 DOI: 10.4329/wjr.v13.i6.171] [Cited by in CrossRef: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
571 Rathod SR, Khanuja HK. Automatic Segmentation of COVID-19 Pneumonia Lesions and its Classification from CT images: A Survey. 2021 International Conference on Intelligent Technologies (CONIT) 2021. [DOI: 10.1109/conit51480.2021.9498350] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
572 Singh A, Kaur A, Dhillon A, Ahuja S, Vohra H. Software system to predict the infection in COVID-19 patients using deep learning and web of things. Softw Pract Exp 2021. [PMID: 34538962 DOI: 10.1002/spe.3011] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
573 Nabavi S, Ejmalian A, Moghaddam ME, Abin AA, Frangi AF, Mohammadi M, Rad HS. Medical imaging and computational image analysis in COVID-19 diagnosis: A review. Comput Biol Med 2021;135:104605. [PMID: 34175533 DOI: 10.1016/j.compbiomed.2021.104605] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
574 Dey S, Bhattacharya R, Malakar S, Mirjalili S, Sarkar R. Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection. Comput Biol Med 2021;135:104585. [PMID: 34229144 DOI: 10.1016/j.compbiomed.2021.104585] [Cited by in Crossref: 17] [Cited by in F6Publishing: 18] [Article Influence: 8.5] [Reference Citation Analysis]
575 Shorfuzzaman M. IoT-enabled stacked ensemble of deep neural networks for the diagnosis of COVID-19 using chest CT scans. Computing. [DOI: 10.1007/s00607-021-00971-5] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
576 Zheng R, Zheng Y, Dong-ye C, Nazir S. Improved 3D U-Net for COVID-19 Chest CT Image Segmentation. Scientific Programming 2021;2021:1-9. [DOI: 10.1155/2021/9999368] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
577 Ramdani H, Allali N, Chat L, El Haddad S. Covid-19 imaging: A narrative review. Ann Med Surg (Lond) 2021;69:102489. [PMID: 34178312 DOI: 10.1016/j.amsu.2021.102489] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
578 Ding W, Nayak J, Swapnarekha H, Abraham A, Naik B, Pelusi D. Fusion of intelligent learning for COVID-19: A state-of-the-art review and analysis on real medical data. Neurocomputing 2021;457:40-66. [PMID: 34149184 DOI: 10.1016/j.neucom.2021.06.024] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
579 Al-azawi RJ, Al-saidi NM, Jalab HA, Kahtan H, Ibrahim RW. Efficient classification of COVID-19 CT scans by using q-transform model for feature extraction. PeerJ Computer Science 2021;7:e553. [DOI: 10.7717/peerj-cs.553] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
580 Polat Ö. Detection of Covid-19 from Chest CT Images using Xception Architecture: A Deep Transfer Learning based Approach. Sakarya University Journal of Science 2021;25:813-23. [DOI: 10.16984/saufenbilder.903886] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
581 Arora V, Ng EY, Leekha RS, Darshan M, Singh A. Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan. Comput Biol Med 2021;135:104575. [PMID: 34153789 DOI: 10.1016/j.compbiomed.2021.104575] [Cited by in Crossref: 12] [Cited by in F6Publishing: 9] [Article Influence: 6.0] [Reference Citation Analysis]
582 Surianarayanan C, Chelliah PR. Leveraging Artificial Intelligence (AI) Capabilities for COVID-19 Containment. New Gener Comput 2021;:1-25. [PMID: 34131359 DOI: 10.1007/s00354-021-00128-0] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
583 Madhavan MV, Khamparia A, Gupta D, Pande S, Tiwari P, Hossain MS. Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning. Neural Comput Appl 2021;:1-14. [PMID: 34127892 DOI: 10.1007/s00521-021-06171-8] [Cited by in Crossref: 7] [Cited by in F6Publishing: 10] [Article Influence: 3.5] [Reference Citation Analysis]
584 Elsharkawy M, Sharafeldeen A, Taher F, Shalaby A, Soliman A, Mahmoud A, Ghazal M, Khalil A, Alghamdi NS, Razek AAKA, Alnaghy E, El-Melegy MT, Sandhu HS, Giridharan GA, El-Baz A. Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images. Sci Rep 2021;11:12095. [PMID: 34103587 DOI: 10.1038/s41598-021-91305-0] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
585 [DOI: 10.1109/icassp39728.2021.9414426] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
586 Heidarian S, Afshar P, Mohammadi A, Md MJR, Md AO, Plataniotis KN, Naderkhani F. Ct-Caps: Feature Extraction-Based Automated Framework for Covid-19 Disease Identification From Chest Ct Scans Using Capsule Networks. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021. [DOI: 10.1109/icassp39728.2021.9414214] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
587 Yang Z, Hou Y, Chen Z, Zhang L, Chen J. A Multi-Stage Progressive Learning Strategy for Covid-19 Diagnosis Using Chest Computed Tomography with Imbalanced Data. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021. [DOI: 10.1109/icassp39728.2021.9414745] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
588 Jiang Y, Chen H, Ko H, Han DK. Few-Shot Learning for Ct Scan Based Covid-19 Diagnosis. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021. [DOI: 10.1109/icassp39728.2021.9413443] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
589 Li B, Zhang Q, Song Y, Zhao Z, Meng Z, Su F. Diagnosing Covid-19 from CT Images Based on an Ensemble Learning Framework. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021. [DOI: 10.1109/icassp39728.2021.9413707] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
590 [DOI: 10.1109/icassp39728.2021.9414031] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
591 Miyake S, Higurashi T, Jono T, Akimoto T, Ogawa F, Oi Y, Tanaka K, Hara Y, Kobayashi N, Kato H, Yamashiro T, Utsunomiya D, Nakajima A, Yamamoto T, Maeda S, Kaneko T, Takeuchi I. Real-world evaluation of a computed tomography-first triage strategy for suspected Coronavirus disease 2019 in outpatients in Japan: An observational cohort study. Medicine (Baltimore) 2021;100:e26161. [PMID: 34087874 DOI: 10.1097/MD.0000000000026161] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
592 Guggenberger T, Lockl J, Röglinger M, Schlatt V, Sedlmeir J, Stoetzer J, Urbach N, Völter F. Emerging Digital Technologies to Combat Future Crises: Learnings From COVID-19 to be Prepared for the Future. Int J Innovation Technol Management 2021;18:2140002. [DOI: 10.1142/s0219877021400022] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
593 Roth H, Xu Z, Diez CT, Jacob RS, Zember J, Molto J, Li W, Xu S, Turkbey B, Turkbey E, Yang D, Harouni A, Rieke N, Hu S, Isensee F, Tang C, Yu Q, Sölter J, Zheng T, Liauchuk V, Zhou Z, Moltz J, Oliveira B, Xia Y, Maier-Hein K, Li Q, Husch A, Zhang L, Kovalev V, Kang L, Hering A, Vilaça J, Flores M, Xu D, Wood B, Linguraru M. Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge. Res Sq 2021:rs. [PMID: 34100010 DOI: 10.21203/rs.3.rs-571332/v1] [Cited by in Crossref: 8] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
594 Alruwaili M, Shehab A, Abd El-Ghany S. COVID-19 Diagnosis Using an Enhanced Inception-ResNetV2 Deep Learning Model in CXR Images. J Healthc Eng 2021;2021:6658058. [PMID: 34188790 DOI: 10.1155/2021/6658058] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
595 Habib N, Rahman MM. Diagnosis of corona diseases from associated genes and X-ray images using machine learning algorithms and deep CNN. Inform Med Unlocked 2021;24:100621. [PMID: 34075341 DOI: 10.1016/j.imu.2021.100621] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
596 Gresser E, Reich J, Sabel BO, Kunz WG, Fabritius MP, Rübenthaler J, Ingrisch M, Wassilowsky D, Irlbeck M, Ricke J, Puhr-Westerheide D. Risk Stratification for ECMO Requirement in COVID-19 ICU Patients Using Quantitative Imaging Features in CT Scans on Admission. Diagnostics (Basel) 2021;11:1029. [PMID: 34205176 DOI: 10.3390/diagnostics11061029] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
597 Mahmud T, Rahman MA, Fattah SA, Kung S. CovSegNet: A Multi Encoder–Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans. IEEE Trans Artif Intell 2021;2:283-97. [DOI: 10.1109/tai.2021.3064913] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
598 Equbal A, Akhter S, Sood AK, Equbal I. The usefulness of additive manufacturing (AM) in COVID-19. Annals of 3D Printed Medicine 2021;2:100013. [DOI: 10.1016/j.stlm.2021.100013] [Cited by in Crossref: 5] [Cited by in F6Publishing: 10] [Article Influence: 2.5] [Reference Citation Analysis]
599 Kumar A, Choudhary A, Kumar A, Vishwakarma DK. Classification and Comparison of Covid-19 Patients. 2021 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C) 2021. [DOI: 10.1109/icdi3c53598.2021.00022] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
600 [DOI: 10.1109/cbms52027.2021.00103] [Cited by in Crossref: 16] [Cited by in F6Publishing: 17] [Article Influence: 8.0] [Reference Citation Analysis]
601 Dabbagh R, Jamal A, Temsah M, Masud JHB, Titi M, Amer Y, Alkubeyyer M, Alhazmi T, Baothman F, Hneiny L. Machine learning models for predicting diagnosis or prognosis of COVID-19: A systematic review. Computer Methods and Programs in Biomedicine 2021;205:105993. [DOI: 10.1016/j.cmpb.2021.105993] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
602 Zheng W, Yan L, Gou C, Zhang ZC, Jason Zhang J, Hu M, Wang FY. Pay attention to doctor-patient dialogues: Multi-modal knowledge graph attention image-text embedding for COVID-19 diagnosis. Inf Fusion 2021;75:168-85. [PMID: 34093095 DOI: 10.1016/j.inffus.2021.05.015] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
603 Rehouma R, Buchert M, Chen YP. Machine learning for medical imaging‐based COVID‐19 detection and diagnosis. Int J Intell Syst 2021;36:5085-115. [DOI: 10.1002/int.22504] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
604 Kao YS, Lin KT. A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia. Diagnostics (Basel) 2021;11:991. [PMID: 34072573 DOI: 10.3390/diagnostics11060991] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
605 Khanday NY, Sofi SA. Deep insight: Convolutional neural network and its applications for COVID-19 prognosis. Biomed Signal Process Control 2021;69:102814. [PMID: 34093724 DOI: 10.1016/j.bspc.2021.102814] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
606 Kumar V, Singh D, Kaur M, Damaševičius R. Overview of current state of research on the application of artificial intelligence techniques for COVID-19. PeerJ Comput Sci 2021;7:e564. [PMID: 34141890 DOI: 10.7717/peerj-cs.564] [Cited by in Crossref: 26] [Cited by in F6Publishing: 28] [Article Influence: 13.0] [Reference Citation Analysis]
607 Ibrahim MR, Youssef SM, Fathalla KM. Abnormality detection and intelligent severity assessment of human chest computed tomography scans using deep learning: a case study on SARS-COV-2 assessment. J Ambient Intell Humaniz Comput 2021;:1-24. [PMID: 34055098 DOI: 10.1007/s12652-021-03282-x] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
608 Heidarian S, Afshar P, Enshaei N, Naderkhani F, Rafiee MJ, Babaki Fard F, Samimi K, Atashzar SF, Oikonomou A, Plataniotis KN, Mohammadi A. COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans. Front Artif Intell 2021;4:598932. [PMID: 34113843 DOI: 10.3389/frai.2021.598932] [Cited by in Crossref: 43] [Cited by in F6Publishing: 43] [Article Influence: 21.5] [Reference Citation Analysis]
609 Kato S, Ishiwata Y, Aoki R, Iwasawa T, Hagiwara E, Ogura T, Utsunomiya D. Imaging of COVID-19: An update of current evidences. Diagn Interv Imaging 2021;102:493-500. [PMID: 34088635 DOI: 10.1016/j.diii.2021.05.006] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]
610 Rahman MM, Khatun F, Uzzaman A, Sami SI, Bhuiyan MA, Kiong TS. A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic. Int J Health Serv 2021;51:446-61. [PMID: 33999732 DOI: 10.1177/00207314211017469] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
611 Alhasan M, Hasaneen M. Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic. Comput Med Imaging Graph 2021;91:101933. [PMID: 34082281 DOI: 10.1016/j.compmedimag.2021.101933] [Cited by in Crossref: 12] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
612 Abdulkareem M, Petersen SE. The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype. Front Artif Intell 2021;4:652669. [PMID: 34056579 DOI: 10.3389/frai.2021.652669] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
613 Farrah Dhiba T, Lee H. Finding an Efficient Image Size for Covid-19 Diagnosis using Chest X-Ray Images. 2021 5th International Conference on Medical and Health Informatics 2021. [DOI: 10.1145/3472813.3473217] [Reference Citation Analysis]
614 Yuyun X, Lexi Y, Haochu W, Zhenyu S, Xiangyang G. Early Warning Information for Severe and Critical Patients With COVID-19 Based on Quantitative CT Analysis of Lung Segments. Front Public Health 2021;9:596938. [PMID: 34055706 DOI: 10.3389/fpubh.2021.596938] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
615 Sarkodie BD, Mensah YB. CT scan chest findings in symptomatic COVID-19 patients: a reliable alternative for diagnosis. Ghana Med J 2020;54:97-9. [PMID: 33976447 DOI: 10.4314/gmj.v54i4s.14] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
616 Safiabadi Tali SH, LeBlanc JJ, Sadiq Z, Oyewunmi OD, Camargo C, Nikpour B, Armanfard N, Sagan SM, Jahanshahi-Anbuhi S. Tools and Techniques for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)/COVID-19 Detection. Clin Microbiol Rev 2021;34:e00228-20. [PMID: 33980687 DOI: 10.1128/CMR.00228-20] [Cited by in Crossref: 66] [Cited by in F6Publishing: 77] [Article Influence: 33.0] [Reference Citation Analysis]
617 Shalaby WA, Saad W, Shokair M, Abd El-Samie FE, Dessouky MI. COVID-19 Classification Based on Deep Convolution Neural Network Over a Wireless Network. Wirel Pers Commun 2021;:1-21. [PMID: 33994667 DOI: 10.1007/s11277-021-08523-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
618 Xiao F, Sun R, Sun W, Xu D, Lan L, Li H, Liu H, Xu H. Radiomics analysis of chest CT to predict the overall survival for the severe patients of COVID-19 pneumonia. Phys Med Biol 2021;66. [PMID: 33845467 DOI: 10.1088/1361-6560/abf717] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
619 Elbasi E, Mathew S, Topcu AE, Abdelbaki W. A Survey on Machine Learning and Internet of Things for COVID-19. 2021 IEEE World AI IoT Congress (AIIoT) 2021. [DOI: 10.1109/aiiot52608.2021.9454241] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
620 Chrzan R, Bociąga-Jasik M, Bryll A, Grochowska A, Popiela T. Differences among COVID-19, Bronchopneumonia and Atypical Pneumonia in Chest High Resolution Computed Tomography Assessed by Artificial Intelligence Technology. J Pers Med 2021;11:391. [PMID: 34068751 DOI: 10.3390/jpm11050391] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
621 Kumar P, Bajpai B, Gupta DO, Jain DC, Vimal S. Image recognition of COVID-19 using DarkCovidNet architecture based on convolutional neural network. WJE 2021;19:90-7. [DOI: 10.1108/wje-12-2020-0655] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
622 Adamidi ES, Mitsis K, Nikita KS. Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review. Comput Struct Biotechnol J 2021;19:2833-50. [PMID: 34025952 DOI: 10.1016/j.csbj.2021.05.010] [Cited by in Crossref: 16] [Cited by in F6Publishing: 21] [Article Influence: 8.0] [Reference Citation Analysis]
623 Pal B, Gupta D, Rashed-al-mahfuz M, Alyami SA, Moni MA. Vulnerability in Deep Transfer Learning Models to Adversarial Fast Gradient Sign Attack for COVID-19 Prediction from Chest Radiography Images. Applied Sciences 2021;11:4233. [DOI: 10.3390/app11094233] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
624 Moezzi M, Shirbandi K, Shahvandi HK, Arjmand B, Rahim F. The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis. Inform Med Unlocked 2021;24:100591. [PMID: 33977119 DOI: 10.1016/j.imu.2021.100591] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
625 Chrzan R, Popiela T, Małecki M, Skupień J, Bryll A, Grochowska A. COVID-19 Infection Negative in Nasopharyngeal Swabs but Suspected in Computed Tomography and Confirmed in Bronchoalveolar Lavage Material. Case Rep Infect Dis 2021;2021:6627207. [PMID: 33936822 DOI: 10.1155/2021/6627207] [Reference Citation Analysis]
626 Ahmad A, Safi O, Malebary S, Alesawi S, Alkayal E. Decision Tree Ensembles to Predict Coronavirus Disease 2019 Infection: A Comparative Study. Complexity 2021;2021:1-8. [DOI: 10.1155/2021/5550344] [Reference Citation Analysis]
627 Chaudhary PK, Pachori RB. FBSED based automatic diagnosis of COVID-19 using X-ray and CT images. Comput Biol Med 2021;134:104454. [PMID: 33965836 DOI: 10.1016/j.compbiomed.2021.104454] [Cited by in Crossref: 24] [Cited by in F6Publishing: 19] [Article Influence: 12.0] [Reference Citation Analysis]
628 Yoganandhan A, Rajesh Kanna G, Subhash S, Hebinson Jothi J. Retrospective and prospective application of robots and artificial intelligence in global pandemic and epidemic diseases. Vacunas (English Edition) 2021;22:98-105. [DOI: 10.1016/j.vacune.2020.12.002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
629 Lan L, Sun W, Xu D, Yu M, Xiao F, Hu H, Xu H, Wang X. Artificial intelligence-based approaches for COVID-19 patient management. Intelligent Medicine 2021;1:10-15. [DOI: 10.1016/j.imed.2021.05.005] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
630 Shiri I, Sorouri M, Geramifar P, Nazari M, Abdollahi M, Salimi Y, Khosravi B, Askari D, Aghaghazvini L, Hajianfar G, Kasaeian A, Abdollahi H, Arabi H, Rahmim A, Radmard AR, Zaidi H. Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients. Comput Biol Med 2021;132:104304. [PMID: 33691201 DOI: 10.1016/j.compbiomed.2021.104304] [Cited by in Crossref: 69] [Cited by in F6Publishing: 67] [Article Influence: 34.5] [Reference Citation Analysis]
631 Ibrahim DM, Elshennawy NM, Sarhan AM. Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Comput Biol Med 2021;132:104348. [PMID: 33774272 DOI: 10.1016/j.compbiomed.2021.104348] [Cited by in Crossref: 72] [Cited by in F6Publishing: 69] [Article Influence: 36.0] [Reference Citation Analysis]
632 Chaddad A, Hassan L, Desrosiers C. Deep CNN models for predicting COVID-19 in CT and x-ray images. J Med Imaging (Bellingham) 2021;8:014502. [PMID: 33912622 DOI: 10.1117/1.JMI.8.S1.014502] [Cited by in Crossref: 10] [Cited by in F6Publishing: 13] [Article Influence: 5.0] [Reference Citation Analysis]
633 Mori M, Palumbo D, De Lorenzo R, Broggi S, Compagnone N, Guazzarotti G, Giorgio Esposito P, Mazzilli A, Steidler S, Pietro Vitali G, Del Vecchio A, Rovere Querini P, De Cobelli F, Fiorino C. Robust prediction of mortality of COVID-19 patients based on quantitative, operator-independent, lung CT densitometry. Phys Med 2021;85:63-71. [PMID: 33971530 DOI: 10.1016/j.ejmp.2021.04.022] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
634 Born J, Beymer D, Rajan D, Coy A, Mukherjee VV, Manica M, Prasanna P, Ballah D, Guindy M, Shaham D, Shah PL, Karteris E, Robertus JL, Gabrani M, Rosen-Zvi M. On the role of artificial intelligence in medical imaging of COVID-19. Patterns (N Y) 2021;2:100269. [PMID: 33969323 DOI: 10.1016/j.patter.2021.100269] [Cited by in Crossref: 15] [Cited by in F6Publishing: 19] [Article Influence: 7.5] [Reference Citation Analysis]
635 Tello-Mijares S, Woo L. Computed Tomography Image Processing Analysis in COVID-19 Patient Follow-Up Assessment. J Healthc Eng 2021;2021:8869372. [PMID: 33968356 DOI: 10.1155/2021/8869372] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
636 Poly TN, Islam MM, Li YJ, Alsinglawi B, Hsu MH, Jian WS, Yang HC. Application of Artificial Intelligence for Screening COVID-19 Patients Using Digital Images: Meta-analysis. JMIR Med Inform 2021;9:e21394. [PMID: 33764884 DOI: 10.2196/21394] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
637 Elbishlawi S, Abdelpakey MH, Shehata MS, Mohamed MM. CORONA-Net: Diagnosing COVID-19 from X-ray Images Using Re-Initialization and Classification Networks. J Imaging 2021;7:81. [PMID: 34460677 DOI: 10.3390/jimaging7050081] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
638 Hashem MK, Abbas AS. Survey on using deep learning to detection COVID-19 disease. 2021 1st Babylon International Conference on Information Technology and Science (BICITS) 2021. [DOI: 10.1109/bicits51482.2021.9509924] [Reference Citation Analysis]
639 Kumar H, Fernandez CJ, Kolpattil S, Munavvar M, Pappachan JM. Discrepancies in the clinical and radiological profiles of COVID-19: A case-based discussion and review of literature. World J Radiol 2021;13:75-93. [PMID: 33968311 DOI: 10.4329/wjr.v13.i4.75] [Cited by in CrossRef: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
640 Tang L, Tian C, Meng Y, Xu K. Longitudinal evaluation for COVID-19 chest CT disease progression based on Tchebichef moments. Int J Imaging Syst Technol 2021. [PMID: 34219952 DOI: 10.1002/ima.22583] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
641 Schuster P, Crombé A, Nivet H, Berger A, Pourriol L, Favard N, Chazot A, Alonzo-Lacroix F, Youssof E, Cheikh AB, Balique J, Porta B, Petitpierre F, Bouquet G, Mastier C, Bratan F, Bergerot JF, Thomson V, Banaste N, Gorincour G. Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort. Sci Rep 2021;11:8994. [PMID: 33903624 DOI: 10.1038/s41598-021-88053-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
642 Piccialli F, di Cola VS, Giampaolo F, Cuomo S. The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic. Inf Syst Front 2021;:1-31. [PMID: 33935585 DOI: 10.1007/s10796-021-10131-x] [Cited by in Crossref: 30] [Cited by in F6Publishing: 20] [Article Influence: 15.0] [Reference Citation Analysis]
643 Ghaderzadeh M, Asadi F, Jafari R, Bashash D, Abolghasemi H, Aria M. Deep Convolutional Neural Network-Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study. J Med Internet Res 2021;23:e27468. [PMID: 33848973 DOI: 10.2196/27468] [Cited by in Crossref: 20] [Cited by in F6Publishing: 21] [Article Influence: 10.0] [Reference Citation Analysis]
644 Kumar R, Yeni CM, Utami NA, Masand R, Asrani RK, Patel SK, Kumar A, Yatoo MI, Tiwari R, Natesan S, Vora KS, Nainu F, Bilal M, Dhawan M, Emran TB, Ahmad T, Harapan H, Dhama K. SARS-CoV-2 infection during pregnancy and pregnancy-related conditions: Concerns, challenges, management and mitigation strategies-a narrative review. J Infect Public Health 2021;14:863-75. [PMID: 34118736 DOI: 10.1016/j.jiph.2021.04.005] [Cited by in Crossref: 16] [Cited by in F6Publishing: 18] [Article Influence: 8.0] [Reference Citation Analysis]
645 Montazeri M, ZahediNasab R, Farahani A, Mohseni H, Ghasemian F. Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review. JMIR Med Inform 2021;9:e25181. [PMID: 33735095 DOI: 10.2196/25181] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
646 Rasheed J, Jamil A, Hameed AA, Al-Turjman F, Rasheed A. COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review. Interdiscip Sci 2021;13:153-75. [PMID: 33886097 DOI: 10.1007/s12539-021-00431-w] [Cited by in Crossref: 18] [Cited by in F6Publishing: 11] [Article Influence: 9.0] [Reference Citation Analysis]
647 Yan C, Chang Y, Yu H, Xu J, Huang C, Yang M, Wang Y, Wang D, Yu T, Wei S, Li Z, Gong F, Kou M, Gou W, Zhao Q, Sun P, Jia X, Fan Z, Xu J, Li S, Yang Q. Clinical Factors and Quantitative CT Parameters Associated With ICU Admission in Patients of COVID-19 Pneumonia: A Multicenter Study. Front Public Health 2021;9:648360. [PMID: 33968885 DOI: 10.3389/fpubh.2021.648360] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
648 Mukherjee A, Su A, Rajan K. Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors. J Chem Inf Model 2021;61:2187-97. [PMID: 33872000 DOI: 10.1021/acs.jcim.0c01409] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
649 Giordano FM, Ippolito E, Quattrocchi CC, Greco C, Mallio CA, Santo B, D'Alessio P, Crucitti P, Fiore M, Zobel BB, D'Angelillo RM, Ramella S. Radiation-Induced Pneumonitis in the Era of the COVID-19 Pandemic: Artificial Intelligence for Differential Diagnosis. Cancers (Basel) 2021;13:1960. [PMID: 33921652 DOI: 10.3390/cancers13081960] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
650 Bozkurt F. Derin Öğrenme Tekniklerini Kullanarak Akciğer X-Ray Görüntülerinden COVID-19 Tespiti. European Journal of Science and Technology 2021. [DOI: 10.31590/ejosat.898385] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
651 Zuo X, Chen Y, Ohno-Machado L, Xu H. How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles. Brief Bioinform 2021;22:800-11. [PMID: 33757278 DOI: 10.1093/bib/bbaa331] [Cited by in Crossref: 14] [Cited by in F6Publishing: 15] [Article Influence: 7.0] [Reference Citation Analysis]
652 Yoganandhan A, Rajesh Kanna G, Subhash SD, Hebinson Jothi J. Retrospective and prospective application of robots and artificial intelligence in global pandemic and epidemic diseases. Vacunas 2021;22:98-105. [PMID: 33841058 DOI: 10.1016/j.vacun.2020.12.004] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
653 [DOI: 10.1109/isbi48211.2021.9433806] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
654 Rehman A, Iqbal MA, Xing H, Ahmed I. COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review. Applied Sciences 2021;11:3414. [DOI: 10.3390/app11083414] [Cited by in Crossref: 17] [Cited by in F6Publishing: 19] [Article Influence: 8.5] [Reference Citation Analysis]
655 Sangidong JC, Purnomo HD, Santoso FY. Application of Deep Learning for Early Detection of COVID-19 Using CT-Scan Images. 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT) 2021. [DOI: 10.1109/eiconcit50028.2021.9431887] [Reference Citation Analysis]
656 Shen Y, Zhao JH, Cai Y, Wu Y. Low-risk clinic model in oral surgery clinic during COVID-19 pandemic. J Stomatol Oral Maxillofac Surg 2021:S2468-7855(21)00076-8. [PMID: 33845185 DOI: 10.1016/j.jormas.2021.04.001] [Reference Citation Analysis]
657 Singh S, Sapra P, Garg A, Vishwakarma DK. CNN based Covid-aid: Covid 19 Detection using Chest X-ray. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) 2021. [DOI: 10.1109/iccmc51019.2021.9418407] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
658 Albahli S. A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images. Curr Med Imaging 2021;17:109-19. [PMID: 32496988 DOI: 10.2174/1573405616666200604163954] [Cited by in Crossref: 28] [Cited by in F6Publishing: 29] [Article Influence: 14.0] [Reference Citation Analysis]
659 Pollard-Larkin JM, Briere TM, Kudchadker RJ, Sadagopan R, Nitsch PL, Wang XA, Salehpour M, Wang J, Vedam S, Nelson CL, Sahoo N, Zhu XR, Court LE, Balter PA, Robinson IJ, Yang J, Howell RM, Followill DS, Kry S, Beddar SA, Martel MK. Our Experience Leading a Large Medical Physics Practice During the COVID-19 Pandemic. Adv Radiat Oncol 2021;6:100683. [PMID: 33824935 DOI: 10.1016/j.adro.2021.100683] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
660 Sinha A, Rathi M. COVID-19 prediction using AI analytics for South Korea. Appl Intell (Dordr) 2021;:1-19. [PMID: 34764592 DOI: 10.1007/s10489-021-02352-z] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 3.5] [Reference Citation Analysis]
661 Ayyildiz VA. COVID-19’DA KARDİYOTORASİK RADYOLOJİK GÖRÜNTÜLEME VE YAPAY ZEKANIN ROLÜ. SDÜ Tıp Fakültesi Dergisi 2021. [DOI: 10.17343/sdutfd.902875] [Reference Citation Analysis]
662 [DOI: 10.1109/caida51941.2021.9425183] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
663 Vangipuram SK, Appusamy R. MACHINE LEARNING FRAMEWORK FOR COVID-19 DIAGNOSIS. International Conference on Data Science, E-learning and Information Systems 2021 2021. [DOI: 10.1145/3460620.3460624] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
664 Biebau C, Dubbeldam A, Cockmartin L, Coudyze W, Coolen J, Verschakelen J, De Wever W. Comparing Visual Scoring of Lung Injury with a Quantifying AI-Based Scoring in Patients with COVID-19. J Belg Soc Radiol 2021;105:16. [PMID: 33870080 DOI: 10.5334/jbsr.2330] [Reference Citation Analysis]
665 Hazra S, Kumar A, Ganguli S. Application of Artificial Intelligence for Coronavirus (COVID-19) Disease Management. Health Informatics and Technological Solutions for Coronavirus (COVID-19) 2021. [DOI: 10.1201/9781003161066-22] [Reference Citation Analysis]
666 Tsai EB, Simpson S, Lungren MP, Hershman M, Roshkovan L, Colak E, Erickson BJ, Shih G, Stein A, Kalpathy-Cramer J, Shen J, Hafez M, John S, Rajiah P, Pogatchnik BP, Mongan J, Altinmakas E, Ranschaert ER, Kitamura FC, Topff L, Moy L, Kanne JP, Wu CC. The RSNA International COVID-19 Open Radiology Database (RICORD). Radiology 2021;299:E204-13. [PMID: 33399506 DOI: 10.1148/radiol.2021203957] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
667 Zelter PM, Kolsanov AV, Chaplygin SS, Pervushkin SS. Visual and automatic evaluation of the volume of lung damage on computer tomography with pneumonia caused by COVID-19. jour 2021. [DOI: 10.20340/vmi-rvz.2020.6.1] [Reference Citation Analysis]
668 Grodecki K, Killekar A, Lin A, Cadet S, McElhinney P, Razipour A, Chan C, Pressman BD, Julien P, Simon J, Maurovich-Horvat P, Gaibazzi N, Thakur U, Mancini E, Agalbato C, Munechika J, Matsumoto H, Menè R, Parati G, Cernigliaro F, Nerlekar N, Torlasco C, Pontone G, Dey D, Slomka PJ. Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks. ArXiv 2021. [PMID: 33821209] [Reference Citation Analysis]
669 Ebrahiminik H, Nikpour S, Yazdi HR, Mohammadi A, Mirza-Aghazadeh-Attari M. Successful vascular interventional management of superior mesenteric vein thrombosis in a patient with COVID-19: A case report and review of literature. Radiol Case Rep 2021;16:1539-42. [PMID: 33777280 DOI: 10.1016/j.radcr.2021.03.038] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
670 Sähn MJ, Yüksel C, Keil S, Zeisberger MP, Post M, Kleines M, Brokmann JC, Hübel C, Kuhl CK, Isfort P, Schulze-Hagen MF. Accuracy of Chest CT for Differentiating COVID-19 from COVID-19 Mimics. Rofo 2021;193:1081-91. [PMID: 33772486 DOI: 10.1055/a-1388-7950] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
671 Lampejo T. Pneumocystis pneumonia: An important consideration when investigating artificial intelligence-based methods in the radiological diagnosis of COVID-19. Clin Imaging 2021:S0899-7071(21)00135-2. [PMID: 33810937 DOI: 10.1016/j.clinimag.2021.02.044] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
672 Mohammad-Rahimi H, Nadimi M, Ghalyanchi-Langeroudi A, Taheri M, Ghafouri-Fard S. Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review. Front Cardiovasc Med 2021;8:638011. [PMID: 33842563 DOI: 10.3389/fcvm.2021.638011] [Cited by in Crossref: 34] [Cited by in F6Publishing: 38] [Article Influence: 17.0] [Reference Citation Analysis]
673 Tang L, Li C. Artificial intelligent for speech reproduction of information and knowledge of ancient books. 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS) 2021. [DOI: 10.1109/icais50930.2021.9395993] [Reference Citation Analysis]
674 Ehwerhemuepha L, Danioko S, Verma S, Marano R, Feaster W, Taraman S, Moreno T, Zheng J, Yaghmaei E, Chang A. A super learner ensemble of 14 statistical learning models for predicting COVID-19 severity among patients with cardiovascular conditions. Intell Based Med 2021;5:100030. [PMID: 33748802 DOI: 10.1016/j.ibmed.2021.100030] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
675 [DOI: 10.1145/3412841.3441943] [Cited by in Crossref: 20] [Cited by in F6Publishing: 7] [Article Influence: 10.0] [Reference Citation Analysis]
676 Siddiqui MA, Ali MA, Deriche M. On the Early Detection of COVID19 using Advanced Machine Learning Techniques: A Review. 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD) 2021. [DOI: 10.1109/ssd52085.2021.9429345] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
677 Lorenzen SS, Nielsen M, Jimenez-solem E, Petersen TS, Perner A, Thorsen-meyer H, Igel C, Sillesen M. Developing Machine Learning Models for Predicting Intensive Care Unit Resource Use During the COVID-19 Pandemic.. [DOI: 10.1101/2021.03.19.21253947] [Reference Citation Analysis]
678 Voulodimos A, Protopapadakis E, Katsamenis I, Doulamis A, Doulamis N. A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images. Sensors (Basel) 2021;21:2215. [PMID: 33810066 DOI: 10.3390/s21062215] [Cited by in Crossref: 23] [Cited by in F6Publishing: 28] [Article Influence: 11.5] [Reference Citation Analysis]
679 Mohammadzadeh Z, Maserat E, Kariminezhad R. Application of Information Technology Models, Approaches and Tools in Covid-19 Management: Rapid Review. Depiction of Health 2021;12:77-95. [DOI: 10.34172/doh.2021.09] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
680 Han C, Li M, Haihambo N, Babuna P, Liu Q, Zhao X, Jaeger C, Li Y, Yang S. Mechanisms of recurrent outbreak of COVID-19: a model-based study. Nonlinear Dyn 2021;:1-17. [PMID: 33758464 DOI: 10.1007/s11071-021-06371-w] [Cited by in Crossref: 12] [Cited by in F6Publishing: 14] [Article Influence: 6.0] [Reference Citation Analysis]
681 Feng Y, Liu S, Cheng Z, Quiroz JC, Rezazadegan D, Chen P, Lin Q, Qian L, Liu X, Berkovsky S, Coiera E, Song L, Qiu X, Cai X. Severity Assessment and Progression Prediction of COVID-19 Patients based on the LesionEncoder Framework and Chest CT (Preprint).. [DOI: 10.2196/preprints.28903] [Reference Citation Analysis]
682 Salvatore C, Interlenghi M, Monti CB, Ippolito D, Capra D, Cozzi A, Schiaffino S, Polidori A, Gandola D, Alì M, Castiglioni I, Messa C, Sardanelli F. Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia. Diagnostics (Basel) 2021;11:530. [PMID: 33809625 DOI: 10.3390/diagnostics11030530] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
683 Irfan M, Iftikhar MA, Yasin S, Draz U, Ali T, Hussain S, Bukhari S, Alwadie AS, Rahman S, Glowacz A, Althobiani F. Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19. Int J Environ Res Public Health 2021;18:3056. [PMID: 33809665 DOI: 10.3390/ijerph18063056] [Cited by in Crossref: 31] [Cited by in F6Publishing: 36] [Article Influence: 15.5] [Reference Citation Analysis]
684 Ghaderzadeh M, Asadi F. Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review. J Healthc Eng 2021;2021:6677314. [PMID: 33747419 DOI: 10.1155/2021/6677314] [Cited by in Crossref: 22] [Cited by in F6Publishing: 29] [Article Influence: 11.0] [Reference Citation Analysis]
685 Chatzitofis A, Cancian P, Gkitsas V, Carlucci A, Stalidis P, Albanis G, Karakottas A, Semertzidis T, Daras P, Giannitto C, Casiraghi E, Sposta FM, Vatteroni G, Ammirabile A, Lofino L, Ragucci P, Laino ME, Voza A, Desai A, Cecconi M, Balzarini L, Chiti A, Zarpalas D, Savevski V. Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment. Int J Environ Res Public Health 2021;18:2842. [PMID: 33799509 DOI: 10.3390/ijerph18062842] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
686 Gülbay M, Özbay BO, Mendi BAR, Baştuğ A, Bodur H. A CT radiomics analysis of COVID-19-related ground-glass opacities and consolidation: Is it valuable in a differential diagnosis with other atypical pneumonias? PLoS One 2021;16:e0246582. [PMID: 33690730 DOI: 10.1371/journal.pone.0246582] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
687 [DOI: 10.1109/southeastcon45413.2021.9401826] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
688 Kasper J, Decker J, Wiesenreiter K, Römmele C, Ebigbo A, Braun G, Häckel T, Schwarz F, Wehler M, Messmann H, Kröncke TJ, Scheurig-Münkler C. Typical Imaging Patterns in COVID-19 Infections of the Lung on Plain Chest Radiographs to Aid Early Triage. Rofo 2021. [PMID: 33694145 DOI: 10.1055/a-1388-8147] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
689 Wang SH, Zhang Y, Cheng X, Zhang X, Zhang YD. PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation. Comput Math Methods Med 2021;2021:6633755. [PMID: 33777167 DOI: 10.1155/2021/6633755] [Cited by in Crossref: 23] [Cited by in F6Publishing: 24] [Article Influence: 11.5] [Reference Citation Analysis]
690 Arntfield R, VanBerlo B, Alaifan T, Phelps N, White M, Chaudhary R, Ho J, Wu D. Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study. BMJ Open 2021;11:e045120. [PMID: 33674378 DOI: 10.1136/bmjopen-2020-045120] [Cited by in Crossref: 27] [Cited by in F6Publishing: 28] [Article Influence: 13.5] [Reference Citation Analysis]
691 Abbaspour Onari M, Yousefi S, Rabieepour M, Alizadeh A, Jahangoshai Rezaee M. A medical decision support system for predicting the severity level of COVID-19. Complex Intell Systems 2021;:1-15. [PMID: 34777959 DOI: 10.1007/s40747-021-00312-1] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
692 Hamed A, Sobhy A, Nassar H. Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm. Arab J Sci Eng 2021;:1-12. [PMID: 33688457 DOI: 10.1007/s13369-020-05212-z] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 3.5] [Reference Citation Analysis]
693 Hesam-Shariati N, Fatehi P, Fathi F, Abouzaripour M, Hesam Shariati MB. A case report of greater saphenous vein thrombosis in a patient with coronavirus (COVID-19) infection. Trop Dis Travel Med Vaccines 2021;7:6. [PMID: 33658082 DOI: 10.1186/s40794-021-00131-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
694 Jiang H, Tang S, Liu W, Zhang Y. Deep learning for COVID-19 chest CT (computed tomography) image analysis: A lesson from lung cancer. Comput Struct Biotechnol J 2021;19:1391-9. [PMID: 33680351 DOI: 10.1016/j.csbj.2021.02.016] [Cited by in Crossref: 13] [Cited by in F6Publishing: 8] [Article Influence: 6.5] [Reference Citation Analysis]
695 Rahman S, Sarker S, Miraj MAA, Nihal RA, Nadimul Haque AKM, Noman AA. Deep Learning-Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis. Cognit Comput 2021;:1-30. [PMID: 33680209 DOI: 10.1007/s12559-020-09779-5] [Cited by in Crossref: 12] [Cited by in F6Publishing: 16] [Article Influence: 6.0] [Reference Citation Analysis]
696 Tiwari S, Jain A. Convolutional capsule network for COVID-19 detection using radiography images. Int J Imaging Syst Technol 2021. [PMID: 33821095 DOI: 10.1002/ima.22566] [Cited by in Crossref: 22] [Cited by in F6Publishing: 23] [Article Influence: 11.0] [Reference Citation Analysis]
697 Elmuogy S, Hikal NA, Hassan E. An efficient technique for CT scan images classification of COVID-19. IFS 2021;40:5225-38. [DOI: 10.3233/jifs-201985] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
698 Khan MA. An automated and fast system to identify COVID-19 from X-ray radiograph of the chest using image processing and machine learning. Int J Imaging Syst Technol 2021. [PMID: 33821097 DOI: 10.1002/ima.22564] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
699 Maior CBS, Santana JMM, Lins ID, Moura MJC. Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases. PLoS One 2021;16:e0247839. [PMID: 33647062 DOI: 10.1371/journal.pone.0247839] [Cited by in Crossref: 10] [Cited by in F6Publishing: 13] [Article Influence: 5.0] [Reference Citation Analysis]
700 Suri JS, Agarwal S, Gupta SK, Puvvula A, Biswas M, Saba L, Bit A, Tandel GS, Agarwal M, Patrick A, Faa G, Singh IM, Oberleitner R, Turk M, Chadha PS, Johri AM, Miguel Sanches J, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou A, Misra DP, Agarwal V, Kitas GD, Ahluwalia P, Teji J, Al-Maini M, Dhanjil SK, Sockalingam M, Saxena A, Nicolaides A, Sharma A, Rathore V, Ajuluchukwu JNA, Fatemi M, Alizad A, Viswanathan V, Krishnan PK, Naidu S. A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence. Comput Biol Med 2021;130:104210. [PMID: 33550068 DOI: 10.1016/j.compbiomed.2021.104210] [Cited by in Crossref: 35] [Cited by in F6Publishing: 34] [Article Influence: 17.5] [Reference Citation Analysis]
701 Hasan SMM, Rabbi MF, Champa AI, Zaman MA. A Comparative Study of Classification Approaches for COVID-19 Prediction. 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD) 2021. [DOI: 10.1109/icict4sd50815.2021.9396890] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
702 Zheng F, Li L, Zhang X, Song Y, Huang Z, Chong Y, Chen Z, Zhu H, Wu J, Chen W, Lu Y, Yang Y, Zha Y, Zhao H, Shen J. Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning. Interdiscip Sci 2021;13:273-85. [PMID: 33641077 DOI: 10.1007/s12539-021-00420-z] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
703 Zhang Q, Chen Z, Liu G, Zhang W, Du Q, Tan J, Gao Q. Artificial Intelligence Clinicians Can Use Chest Computed Tomography Technology to Automatically Diagnose Coronavirus Disease 2019 (COVID-19) Pneumonia and Enhance Low-Quality Images. Infect Drug Resist 2021;14:671-87. [PMID: 33658806 DOI: 10.2147/IDR.S296346] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
704 Madaan V, Roy A, Gupta C, Agrawal P, Sharma A, Bologa C, Prodan R. XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks. New Gener Comput 2021;:1-15. [PMID: 33642663 DOI: 10.1007/s00354-021-00121-7] [Cited by in Crossref: 18] [Cited by in F6Publishing: 22] [Article Influence: 9.0] [Reference Citation Analysis]
705 Liu T, Tsang W, Huang F, Lau OY, Chen Y, Sheng J, Guo Y, Akinwunmi B, Zhang CJ, Ming WK. Patients' Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment. J Med Internet Res 2021;23:e22841. [PMID: 33493130 DOI: 10.2196/22841] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
706 Wang Y, Zhang Y, Liu Y, Tian J, Zhong C, Shi Z, Zhang Y, He Z. Does non-COVID-19 lung lesion help? investigating transferability in COVID-19 CT image segmentation. Comput Methods Programs Biomed 2021;202:106004. [PMID: 33662804 DOI: 10.1016/j.cmpb.2021.106004] [Cited by in Crossref: 22] [Cited by in F6Publishing: 24] [Article Influence: 11.0] [Reference Citation Analysis]
707 Razek A, Fouda N, Fahmy D, Tanatawy MS, Sultan A, Bilal M, Zaki M, Abdel-Aziz M, Sobh D. Computed tomography of the chest in patients with COVID-19: what do radiologists want to know? Pol J Radiol 2021;86:e122-35. [PMID: 33758638 DOI: 10.5114/pjr.2021.104049] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
708 Arabi YM, Azoulay E, Al-Dorzi HM, Phua J, Salluh J, Binnie A, Hodgson C, Angus DC, Cecconi M, Du B, Fowler R, Gomersall CD, Horby P, Juffermans NP, Kesecioglu J, Kleinpell RM, Machado FR, Martin GS, Meyfroidt G, Rhodes A, Rowan K, Timsit JF, Vincent JL, Citerio G. How the COVID-19 pandemic will change the future of critical care. Intensive Care Med 2021;47:282-91. [PMID: 33616696 DOI: 10.1007/s00134-021-06352-y] [Cited by in Crossref: 69] [Cited by in F6Publishing: 75] [Article Influence: 34.5] [Reference Citation Analysis]
709 Guo R, Hu X, Song H, Xu P, Xu H, Rominger A, Lin X, Menze B, Li B, Shi K. Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type. Eur J Nucl Med Mol Imaging 2021;48:3151-61. [PMID: 33611614 DOI: 10.1007/s00259-021-05232-3] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
710 Elzeki OM, Shams M, Sarhan S, Abd Elfattah M, Hassanien AE. COVID-19: a new deep learning computer-aided model for classification. PeerJ Comput Sci 2021;7:e358. [PMID: 33817008 DOI: 10.7717/peerj-cs.358] [Cited by in Crossref: 16] [Cited by in F6Publishing: 18] [Article Influence: 8.0] [Reference Citation Analysis]
711 Liu B, Liu P, Dai L, Yang Y, Xie P, Tan Y, Du J, Shan W, Zhao C, Zhong Q, Lin X, Guan X, Xing N, Sun Y, Wang W, Zhang Z, Fu X, Fan Y, Li M, Zhang N, Li L, Liu Y, Xu L, Du J, Zhao Z, Hu X, Fan W, Wang R, Wu C, Nie Y, Cheng L, Ma L, Li Z, Jia Q, Liu M, Guo H, Huang G, Shen H, Zhang L, Zhang P, Guo G, Li H, An W, Zhou J, He K. Assisting scalable diagnosis automatically via CT images in the combat against COVID-19. Sci Rep 2021;11:4145. [PMID: 33603047 DOI: 10.1038/s41598-021-83424-5] [Cited by in Crossref: 4] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
712 Majumdar S, Verma R, Saha A, Bhattacharyya P, Maji P, Surjit M, Kundu M, Basu J, Saha S. Perspectives About Modulating Host Immune System in Targeting SARS-CoV-2 in India. Front Genet 2021;12:637362. [PMID: 33664772 DOI: 10.3389/fgene.2021.637362] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
713 Quiroz JC, Feng YZ, Cheng ZY, Rezazadegan D, Chen PK, Lin QT, Qian L, Liu XF, Berkovsky S, Coiera E, Song L, Qiu X, Liu S, Cai XR. Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study. JMIR Med Inform 2021;9:e24572. [PMID: 33534723 DOI: 10.2196/24572] [Cited by in Crossref: 21] [Cited by in F6Publishing: 22] [Article Influence: 10.5] [Reference Citation Analysis]
714 Albahli S, Yar GNAH. Fast and Accurate Detection of COVID-19 Along With 14 Other Chest Pathologies Using a Multi-Level Classification: Algorithm Development and Validation Study. J Med Internet Res 2021;23:e23693. [PMID: 33529154 DOI: 10.2196/23693] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 7.0] [Reference Citation Analysis]
715 Pham Q, Gamble A, Hearn J, Cafazzo JA. The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations. J Med Internet Res 2021;23:e22320. [PMID: 33565982 DOI: 10.2196/22320] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 3.0] [Reference Citation Analysis]
716 Kanne JP, Bai H, Bernheim A, Chung M, Haramati LB, Kallmes DF, Little BP, Rubin G, Sverzellati N. COVID-19 Imaging: What We Know Now and What Remains Unknown. Radiology. 2021;204522. [PMID: 33560192 DOI: 10.1148/radiol.2021204522] [Cited by in Crossref: 40] [Cited by in F6Publishing: 48] [Article Influence: 20.0] [Reference Citation Analysis]
717 Zhu Z, Xingming Z, Tao G, Dan T, Li J, Chen X, Li Y, Zhou Z, Zhang X, Zhou J, Chen D, Wen H, Cai H. Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort. Interdiscip Sci 2021;13:73-82. [PMID: 33565027 DOI: 10.1007/s12539-020-00408-1] [Cited by in Crossref: 14] [Cited by in F6Publishing: 17] [Article Influence: 7.0] [Reference Citation Analysis]
718 Sun C, Hong S, Song M, Li H, Wang Z. Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning. BMC Med Inform Decis Mak 2021;21:45. [PMID: 33557818 DOI: 10.1186/s12911-020-01359-9] [Cited by in Crossref: 15] [Cited by in F6Publishing: 19] [Article Influence: 7.5] [Reference Citation Analysis]
719 Wan Y, Zhou H, Zhang X. An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis. Entropy (Basel) 2021;23:204. [PMID: 33562309 DOI: 10.3390/e23020204] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
720 Singh D, Kumar V, Kaur M. Densely connected convolutional networks-based COVID-19 screening model. Appl Intell 2021;51:3044-51. [DOI: 10.1007/s10489-020-02149-6] [Cited by in Crossref: 49] [Cited by in F6Publishing: 22] [Article Influence: 24.5] [Reference Citation Analysis]
721 Mallio CA, Napolitano A, Castiello G, Giordano FM, D'Alessio P, Iozzino M, Sun Y, Angeletti S, Russano M, Santini D, Tonini G, Zobel BB, Vincenzi B, Quattrocchi CC. Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis. Cancers (Basel) 2021;13:652. [PMID: 33562011 DOI: 10.3390/cancers13040652] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
722 Jimenez-Solem E, Petersen TS, Hansen C, Hansen C, Lioma C, Igel C, Boomsma W, Krause O, Lorenzen S, Selvan R, Petersen J, Nyeland ME, Ankarfeldt MZ, Virenfeldt GM, Winther-Jensen M, Linneberg A, Ghazi MM, Detlefsen N, Lauritzen AD, Smith AG, de Bruijne M, Ibragimov B, Petersen J, Lillholm M, Middleton J, Mogensen SH, Thorsen-Meyer HC, Perner A, Helleberg M, Kaas-Hansen BS, Bonde M, Bonde A, Pai A, Nielsen M, Sillesen M. Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients. Sci Rep 2021;11:3246. [PMID: 33547335 DOI: 10.1038/s41598-021-81844-x] [Cited by in Crossref: 34] [Cited by in F6Publishing: 39] [Article Influence: 17.0] [Reference Citation Analysis]
723 Gong K, Wu D, Arru CD, Homayounieh F, Neumark N, Guan J, Buch V, Kim K, Bizzo BC, Ren H, Tak WY, Park SY, Lee YR, Kang MK, Park JG, Carriero A, Saba L, Masjedi M, Talari H, Babaei R, Mobin HK, Ebrahimian S, Guo N, Digumarthy SR, Dayan I, Kalra MK, Li Q. A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records. Eur J Radiol 2021;139:109583. [PMID: 33846041 DOI: 10.1016/j.ejrad.2021.109583] [Cited by in Crossref: 16] [Cited by in F6Publishing: 17] [Article Influence: 8.0] [Reference Citation Analysis]
724 Efremtsev V, Efremtsev N, Teterin E, Teterin P, Bazavluk E. Chest X-ray image classification for viral pneumonia and Сovid-19 using neural networks. Computer Optics 2021;45:149-53. [DOI: 10.18287/2412-6179-co-765] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
725 Shi W, Peng X, Liu T, Cheng Z, Lu H, Yang S, Zhang J, Wang M, Gao Y, Shi Y, Zhang Z, Shan F. A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients. Ann Transl Med 2021;9:216. [PMID: 33708843 DOI: 10.21037/atm-20-2464] [Cited by in Crossref: 24] [Cited by in F6Publishing: 25] [Article Influence: 12.0] [Reference Citation Analysis]
726 Khan H, Kushwah KK, Singh S, Urkude H, Maurya MR, Sadasivuni KK. Smart technologies driven approaches to tackle COVID-19 pandemic: a review. 3 Biotech 2021;11:50. [PMID: 33457174 DOI: 10.1007/s13205-020-02581-y] [Cited by in Crossref: 29] [Cited by in F6Publishing: 11] [Article Influence: 14.5] [Reference Citation Analysis]
727 Kang M, Hong KS, Chikontwe P, Luna M, Jang JG, Park J, Shin KC, Park SH, Ahn JH. Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia Patients: a Deep Learning Perspective. J Korean Med Sci 2021;36:e46. [PMID: 33527788 DOI: 10.3346/jkms.2021.36.e46] [Cited by in Crossref: 14] [Cited by in F6Publishing: 9] [Article Influence: 7.0] [Reference Citation Analysis]
728 Blain M, Kassin MT, Varble N, Wang X, Xu Z, Xu D, Carrafiello G, Vespro V, Stellato E, Ierardi AM, Meglio LD, D Suh R, A Walker S, Xu S, H Sanford T, B Turkbey E, Harmon S, Turkbey B, J Wood B. Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images. Diagn Interv Radiol 2021;27:20-7. [PMID: 32815519 DOI: 10.5152/dir.2020.20205] [Cited by in Crossref: 26] [Cited by in F6Publishing: 30] [Article Influence: 13.0] [Reference Citation Analysis]
729 Roland D, Stansfield BK. Every cloud: how the COVID-19 pandemic may benefit child health. Pediatr Res 2021;89:413-4. [PMID: 32369827 DOI: 10.1038/s41390-020-0947-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
730 Lee EH, Zheng J, Colak E, Mohammadzadeh M, Houshmand G, Bevins N, Kitamura F, Altinmakas E, Reis EP, Kim JK, Klochko C, Han M, Moradian S, Mohammadzadeh A, Sharifian H, Hashemi H, Firouznia K, Ghanaati H, Gity M, Doğan H, Salehinejad H, Alves H, Seekins J, Abdala N, Atasoy Ç, Pouraliakbar H, Maleki M, Wong SS, Yeom KW. Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT. NPJ Digit Med 2021;4:11. [PMID: 33514852 DOI: 10.1038/s41746-020-00369-1] [Cited by in Crossref: 22] [Cited by in F6Publishing: 23] [Article Influence: 11.0] [Reference Citation Analysis]
731 Mazzilli A, Fiorino C, Loria A, Mori M, Esposito PG, Palumbo D, de Cobelli F, del Vecchio A. An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients. Applied Sciences 2021;11:1238. [DOI: 10.3390/app11031238] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
732 Sharma A, Ahmad Farouk I, Lal SK. COVID-19: A Review on the Novel Coronavirus Disease Evolution, Transmission, Detection, Control and Prevention. Viruses 2021;13:202. [PMID: 33572857 DOI: 10.3390/v13020202] [Cited by in Crossref: 65] [Cited by in F6Publishing: 74] [Article Influence: 32.5] [Reference Citation Analysis]
733 Benameur N, Mahmoudi R, Zaid S, Arous Y, Hmida B, Bedoui MH. SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: A review. Clin Imaging 2021;76:6-14. [PMID: 33545517 DOI: 10.1016/j.clinimag.2021.01.019] [Cited by in Crossref: 13] [Cited by in F6Publishing: 14] [Article Influence: 6.5] [Reference Citation Analysis]
734 Alafif T, Tehame AM, Bajaba S, Barnawi A, Zia S. Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions. Int J Environ Res Public Health 2021;18:1117. [PMID: 33513984 DOI: 10.3390/ijerph18031117] [Cited by in Crossref: 44] [Cited by in F6Publishing: 51] [Article Influence: 22.0] [Reference Citation Analysis]
735 Gupta A, Singh A, Bharadwaj D, Mondal AK. Humans and Robots: A Mutually Inclusive Relationship in a Contagious World. Int J Autom Comput 2021;18:185-203. [DOI: 10.1007/s11633-020-1266-8] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
736 Li J, Zhao G, Tao Y, Zhai P, Chen H, He H, Cai T. Multi-task contrastive learning for automatic CT and X-ray diagnosis of COVID-19. Pattern Recognit 2021;114:107848. [PMID: 33518812 DOI: 10.1016/j.patcog.2021.107848] [Cited by in Crossref: 25] [Cited by in F6Publishing: 27] [Article Influence: 12.5] [Reference Citation Analysis]
737 Joseph Raj AN, Zhu H, Khan A, Zhuang Z, Yang Z, Mahesh VGV, Karthik G. ADID-UNET-a segmentation model for COVID-19 infection from lung CT scans. PeerJ Comput Sci 2021;7:e349. [PMID: 33816999 DOI: 10.7717/peerj-cs.349] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
738 Mottaqi MS, Mohammadipanah F, Sajedi H. Contribution of machine learning approaches in response to SARS-CoV-2 infection. Inform Med Unlocked 2021;23:100526. [PMID: 33869730 DOI: 10.1016/j.imu.2021.100526] [Cited by in Crossref: 20] [Cited by in F6Publishing: 20] [Article Influence: 10.0] [Reference Citation Analysis]
739 Singh S. Pneumonia Detection using Deep Learning. 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE) 2021. [DOI: 10.1109/icnte51185.2021.9487731] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
740 Mansour NA, Saleh AI, Badawy M, Ali HA. Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy. J Ambient Intell Humaniz Comput 2021;:1-33. [PMID: 33469467 DOI: 10.1007/s12652-020-02883-2] [Cited by in Crossref: 15] [Cited by in F6Publishing: 10] [Article Influence: 7.5] [Reference Citation Analysis]
741 Jee G, Harshvardhan G, Gourisaria MK. Juxtaposing inference capabilities of deep neural models over posteroanterior chest radiographs facilitating COVID-19 detection. Journal of Interdisciplinary Mathematics 2021;24:299-325. [DOI: 10.1080/09720502.2020.1838061] [Cited by in Crossref: 13] [Cited by in F6Publishing: 1] [Article Influence: 6.5] [Reference Citation Analysis]
742 Syeda HB, Syed M, Sexton KW, Syed S, Begum S, Syed F, Prior F, Yu F Jr. Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review. JMIR Med Inform 2021;9:e23811. [PMID: 33326405 DOI: 10.2196/23811] [Cited by in Crossref: 51] [Cited by in F6Publishing: 55] [Article Influence: 25.5] [Reference Citation Analysis]
743 Alshazly H, Linse C, Barth E, Martinetz T. Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning. Sensors (Basel) 2021;21:E455. [PMID: 33440674 DOI: 10.3390/s21020455] [Cited by in Crossref: 67] [Cited by in F6Publishing: 73] [Article Influence: 33.5] [Reference Citation Analysis]
744 Xia Y, Chen W, Ren H, Zhao J, Wang L, Jin R, Zhou J, Wang Q, Yan F, Zhang B, Lou J, Wang S, Li X, Zhou J, Xia L, Jin C, Feng J, Li W, Shen H. A rapid screening classifier for diagnosing COVID-19. Int J Biol Sci 2021;17:539-48. [PMID: 33613111 DOI: 10.7150/ijbs.53982] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 6.0] [Reference Citation Analysis]
745 Liu H, Ren H, Wu Z, Xu H, Zhang S, Li J, Hou L, Chi R, Zheng H, Chen Y, Duan S, Li H, Xie Z, Wang D. CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS. J Transl Med 2021;19:29. [PMID: 33413480 DOI: 10.1186/s12967-020-02692-3] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 9.5] [Reference Citation Analysis]
746 Xu M, Ouyang L, Han L, Sun K, Yu T, Li Q, Tian H, Safarnejad L, Zhang H, Gao Y, Bao FS, Chen Y, Robinson P, Ge Y, Zhu B, Liu J, Chen S. Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach. J Med Internet Res 2021;23:e25535. [PMID: 33404516 DOI: 10.2196/25535] [Cited by in Crossref: 18] [Cited by in F6Publishing: 22] [Article Influence: 9.0] [Reference Citation Analysis]
747 Tsai EB, Simpson S, Lungren MP, Hershman M, Roshkovan L, Colak E, Erickson BJ, Shih G, Stein A, Kalpathy-Cramer J, Shen J, Hafez M, John S, Rajiah P, Pogatchnik BP, Mongan J, Altinmakas E, Ranschaert ER, Kitamura FC, Topff L, Moy L, Kanne JP, Wu CC. The RSNA International COVID-19 Open Radiology Database (RICORD). Radiology 2021;299:E204-13. [PMID: 33399506 DOI: 10.1148/radiol.2021203957] [Cited by in Crossref: 46] [Cited by in F6Publishing: 50] [Article Influence: 23.0] [Reference Citation Analysis]
748 Elmokadem AH, Bayoumi D, Abo-hedibah SA, El-morsy A. Diagnostic performance of chest CT in differentiating COVID-19 from other causes of ground-glass opacities. Egypt J Radiol Nucl Med 2021;52. [DOI: 10.1186/s43055-020-00398-6] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
749 Senthilraja M. Application of Artificial Intelligence to Address Issues Related to the COVID-19 Virus. SLAS Technol 2021;26:123-6. [PMID: 33390088 DOI: 10.1177/2472630320983813] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
750 Shastri S, Singh K, Kumar S, Kour P, Mansotra V. Deep-LSTM ensemble framework to forecast Covid-19: an insight to the global pandemic. Int J Inf Technol 2021;:1-11. [PMID: 33426425 DOI: 10.1007/s41870-020-00571-0] [Cited by in Crossref: 12] [Cited by in F6Publishing: 19] [Article Influence: 6.0] [Reference Citation Analysis]
751 Soliman M, Darwish A, Hassanien AE. Deep Learning Technology for Tackling COVID-19 Pandemic. Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches 2021. [DOI: 10.1007/978-3-030-63307-3_9] [Reference Citation Analysis]
752 Gunturu LN, Dornadula G. Integration of Deep Learning Machine Models with Conventional Diagnostic Tools in Medical Image Analysis for Detection and Diagnosis of Novel Coronavirus (COVID-19). Studies in Systems, Decision and Control 2021. [DOI: 10.1007/978-3-030-67716-9_4] [Reference Citation Analysis]
753 Zhou M, Yang D, Chen Y, Xu Y, Xu JF, Jie Z, Yao W, Jin X, Pan Z, Tan J, Wang L, Xia Y, Zou L, Xu X, Wei J, Guan M, Yan F, Feng J, Zhang H, Qu J. Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia. Ann Transl Med 2021;9:111. [PMID: 33569413 DOI: 10.21037/atm-20-5328] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
754 Saxena A, Chandra S. Machine Learning Approaches in Detection and Diagnosis of COVID-19. Artificial Intelligence and Machine Learning in Healthcare 2021. [DOI: 10.1007/978-981-16-0811-7_7] [Reference Citation Analysis]
755 Sandhu A, Sahu KM. Role of Artificial Intelligence in Forecast Analysis of COVID-19 Outbreak. Impact of AI and Data Science in Response to Coronavirus Pandemic 2021. [DOI: 10.1007/978-981-16-2786-6_2] [Reference Citation Analysis]
756 Gasmi A. Machine Learning Techniques for the Identification and Diagnosis of COVID-19. EAI/Springer Innovations in Communication and Computing 2021. [DOI: 10.1007/978-3-030-68936-0_12] [Reference Citation Analysis]
757 Das N, Topalovic M, Janssens W. AIM in Respiratory Disorders. Artificial Intelligence in Medicine 2021. [DOI: 10.1007/978-3-030-58080-3_178-1] [Reference Citation Analysis]
758 Arto M, Al-turjman F. Artificial Intelligence in face of the Novel CoronaVirus. Artificial Intelligence and Machine Learning for COVID-19 2021. [DOI: 10.1007/978-3-030-60188-1_3] [Reference Citation Analysis]
759 Naronglerdrit P, Mporas I, Sheikh-akbari A. COVID-19 detection from chest X-rays using transfer learning with deep convolutional neural networks. Data Science for COVID-19 2021. [DOI: 10.1016/b978-0-12-824536-1.00031-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
760 Gouissem A, Abualsaud K, Yaacoub E, Khattab T, Guizani M. A Novel Pandemic Tracking Map: From Theory to Implementation. IEEE Access 2021;9:51106-51120. [DOI: 10.1109/access.2021.3067824] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
761 Steinhauser S. COVID-19 as a Driver for Digital Transformation in Healthcare. Digitalization in Healthcare 2021. [DOI: 10.1007/978-3-030-65896-0_8] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
762 Bemportato P, Casalino G, Castellano G, Vessio G. Automatic Clustering of CT Scans of COVID-19 Patients Based on Deep Learning. Modeling Decisions for Artificial Intelligence 2021. [DOI: 10.1007/978-3-030-85529-1_19] [Reference Citation Analysis]
763 Raza K, Maryam, Qazi S. An Introduction to Computational Intelligence in COVID-19: Surveillance, Prevention, Prediction, and Diagnosis. Studies in Computational Intelligence 2021. [DOI: 10.1007/978-981-15-8534-0_1] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
764 Nain M, Sharma S, Chaurasia S. Pandemic Management Using Artificial Intelligence-Based Safety Measures. Advances in Medical Technologies and Clinical Practice 2021. [DOI: 10.4018/978-1-7998-7188-0.ch007] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
765 Liu J. Review of Deep Learning-based Approaches for COVID-19 Detection. 2021 2nd International Conference on Computing and Data Science (CDS) 2021. [DOI: 10.1109/cds52072.2021.00069] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
766 Jain R, Singh S, Swami S, kumar S. Deep Learning-Based Techniques to Identify COVID-19 Patients Using Medical Image Segmentation. Health Information Science 2021. [DOI: 10.1007/978-3-030-68723-6_18] [Reference Citation Analysis]
767 Banik S, Banik S, Ghosh A, Mukherjee A. Probabilistic Estimation of COVID-19 Using Patient’s Symptoms. Data Driven Approach Towards Disruptive Technologies 2021. [DOI: 10.1007/978-981-15-9873-9_29] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
768 Chui KT, Lytras MD, Visvizi A, Sarirete A. An overview of artificial intelligence and big data analytics for smart healthcare: requirements, applications, and challenges. Artificial Intelligence and Big Data Analytics for Smart Healthcare 2021. [DOI: 10.1016/b978-0-12-822060-3.00015-2] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
769 Eldow ME. The worldwide methods of artificial intelligence for detection and diagnosis of COVID-19. Leveraging Artificial Intelligence in Global Epidemics 2021. [DOI: 10.1016/b978-0-323-89777-8.00012-9] [Reference Citation Analysis]
770 Mishra S. COVID-19 Detection and Prediction Using Chest X-Ray Images. Intelligent Systems 2021. [DOI: 10.1007/978-981-33-6081-5_23] [Reference Citation Analysis]
771 Al-emran M, Al-kabi MN, Marques G. A Survey of Using Machine Learning Algorithms During the COVID-19 Pandemic. Studies in Systems, Decision and Control 2021. [DOI: 10.1007/978-3-030-67716-9_1] [Cited by in Crossref: 4] [Article Influence: 2.0] [Reference Citation Analysis]
772 Folorunso SO, Ogbuju E, Oladipo F. Artificial Intelligence and the Control of COVID-19: A Review of Machine and Deep Learning Approaches. Artificial Intelligence for COVID-19 2021. [DOI: 10.1007/978-3-030-69744-0_10] [Reference Citation Analysis]
773 Rani S. A Study on COVID-19 Prediction and Detection With Artificial Intelligence-Based Real-Time Healthcare Monitoring Systems. Advances in Medical Technologies and Clinical Practice 2021. [DOI: 10.4018/978-1-7998-7188-0.ch004] [Reference Citation Analysis]
774 Jafari R, Ashtari S, Pourhoseingholi MA, Maghsoudi H, Cheraghalipoor F, Jafari NJ, Saadat H, Rahimi-Bashar F, Vahedian-Azimi A, Sahebkar A. Identification, Monitoring, and Prediction of Disease Severity in Patients with COVID-19 Pneumonia Based on Chest Computed Tomography Scans: A Retrospective Study. Adv Exp Med Biol 2021;1321:265-75. [PMID: 33656732 DOI: 10.1007/978-3-030-59261-5_24] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
775 Corbacho Abelaira MD, Corbacho Abelaira F, Ruano-ravina A, Fernández-villar A. Use of Conventional Chest Imaging and Artificial Intelligence in COVID-19 Infection. A Review of the Literature. Open Respiratory Archives 2021;3:100078. [DOI: 10.1016/j.opresp.2020.100078] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
776 El Habib Daho M, Khouani A, Lazouni MEA, Mahmoudi SA. Explainable Deep Learning Model for COVID-19 Screening in Chest CT Images. Digital Technologies and Applications 2021. [DOI: 10.1007/978-3-030-73882-2_89] [Reference Citation Analysis]
777 Trivedi D, Dave M, Patel R, Dave V, Rathod G. Real-Time COVID-19 Detection and Prediction Using Chest X-rays and CT Scan: A Comparative Study Using AI. Algorithms for Intelligent Systems 2021. [DOI: 10.1007/978-981-33-4604-8_60] [Reference Citation Analysis]
778 Pontone G, Scafuri S, Mancini ME, Agalbato C, Guglielmo M, Baggiano A, Muscogiuri G, Fusini L, Andreini D, Mushtaq S, Conte E, Annoni A, Formenti A, Gennari AG, Guaricci AI, Rabbat MR, Pompilio G, Pepi M, Rossi A. Role of computed tomography in COVID-19. J Cardiovasc Comput Tomogr 2021;15:27-36. [PMID: 32952101 DOI: 10.1016/j.jcct.2020.08.013] [Cited by in Crossref: 52] [Cited by in F6Publishing: 56] [Article Influence: 26.0] [Reference Citation Analysis]
779 Prashant Nagpal, Junfeng Guo, Kyung Min Shin, Jae-Kwang Lim, Ki Beom Kim, Alejandro P Comellas, David W Kaczka, Samuel Peterson, Chang Hyun Lee, Eric A Hoffman. Quantitative CT imaging and advanced visualization methods: potential application in novel coronavirus disease 2019 (COVID-19) pneumonia. BJR Open 2021;3:20200043. [PMID: 33718766 DOI: 10.1259/bjro.20200043] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
780 Abdellaoui Alaoui EA, Koumetio Tekouabou SC, Ougamane I, Chabbar I. Towards Automatic Diagnosis of the COVID-19 Based on Machine Learning. Innovations in Smart Cities Applications Volume 4 2021. [DOI: 10.1007/978-3-030-66840-2_95] [Reference Citation Analysis]
781 Isa A. Computational Intelligence Methods in Medical Image-Based Diagnosis of COVID-19 Infections. Studies in Computational Intelligence 2021. [DOI: 10.1007/978-981-15-8534-0_13] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
782 Uddin MS, Shorif SB, Sarker A. Role-Framework of Artificial Intelligence in Combating the COVID-19 Pandemic. Vision, Sensing and Analytics: Integrative Approaches 2021. [DOI: 10.1007/978-3-030-75490-7_13] [Reference Citation Analysis]
783 Espuny M, da Motta Reis JS, Monteiro Diogo GM, Reis Campos TL, de Mello Santos VH, Ferreira Costa AC, Gonçalves GS, Tasinaffo PM, Vieira Dias LA, da Cunha AM, de Souza Sampaio NA, Rodrigues AM, de Oliveira OJ. COVID-19: The Importance of Artificial Intelligence and Digital Health During a Pandemic. Advances in Intelligent Systems and Computing 2021. [DOI: 10.1007/978-3-030-70416-2_4] [Reference Citation Analysis]
784 Srivastava S, Prithivi PPR, Srija K, Vaishnavi P, Savitha HSS, Grover A, Saxena M, Chandra S, Saxena A. Analysis and visualization of the pandemics using Artificial Intelligence. IOP Conf Ser : Mater Sci Eng 2021;1022:012049. [DOI: 10.1088/1757-899x/1022/1/012049] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
785 Saxena A, Chandra S. Use of Artificial Intelligence in Research and Clinical Decision Making for Combating Mycobacterial Diseases. Artificial Intelligence and Machine Learning in Healthcare 2021. [DOI: 10.1007/978-981-16-0811-7_9] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
786 Beyerstedt S, Casaro EB, Rangel ÉB. COVID-19: angiotensin-converting enzyme 2 (ACE2) expression and tissue susceptibility to SARS-CoV-2 infection. Eur J Clin Microbiol Infect Dis 2021;40:905-19. [PMID: 33389262 DOI: 10.1007/s10096-020-04138-6] [Cited by in Crossref: 214] [Cited by in F6Publishing: 215] [Article Influence: 107.0] [Reference Citation Analysis]
787 Verenich E, Murshed MGS, Khan N, Velasquez A, Hussain F. Mitigating the Class Overlap Problem in Discriminative Localization: COVID-19 and Pneumonia Case Study. Explainable AI Within the Digital Transformation and Cyber Physical Systems 2021. [DOI: 10.1007/978-3-030-76409-8_7] [Reference Citation Analysis]
788 Yadav SL, Dhaiya R, Bhatia S. Conclusions. Researches and Applications of Artificial Intelligence to Mitigate Pandemics 2021. [DOI: 10.1016/b978-0-323-90959-4.00006-7] [Reference Citation Analysis]
789 Surianarayanan C, Chelliah PR. Covid-19 Containment: Demystifying the Research Challenges and Contributions Leveraging Digital Intelligence Technologies. Algorithms for Intelligent Systems 2021. [DOI: 10.1007/978-981-33-4893-6_18] [Reference Citation Analysis]
790 Kose U, Deperlioglu O, Alzubi J, Patrut B. Future of Medical Decision Support Systems. Deep Learning for Medical Decision Support Systems 2021. [DOI: 10.1007/978-981-15-6325-6_10] [Reference Citation Analysis]
791 Markazi DM, Walters K. People’s Perceptions of AI Utilization in the Context of COVID-19. Diversity, Divergence, Dialogue 2021. [DOI: 10.1007/978-3-030-71292-1_5] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
792 Sahoo MK, Khare P, Samant M. Artificial Intelligence-Mediated Medical Diagnosis of COVID-19. Medical Virology: From Pathogenesis to Disease Control 2021. [DOI: 10.1007/978-981-15-7317-0_3] [Reference Citation Analysis]
793 Habuza T, Navaz AN, Hashim F, Alnajjar F, Zaki N, Serhani MA, Statsenko Y. AI applications in robotics, diagnostic image analysis and precision medicine: Current limitations, future trends, guidelines on CAD systems for medicine. Informatics in Medicine Unlocked 2021;24:100596. [DOI: 10.1016/j.imu.2021.100596] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 7.5] [Reference Citation Analysis]
794 Long Y, Zhu S, Tu D. DuCN: Dual-Children Network for Medical Diagnosis and Similar Case Recommendation Towards COVID-19. Lecture Notes in Computer Science 2021. [DOI: 10.1007/978-3-030-90874-4_15] [Reference Citation Analysis]
795 Glauner P. Artificial Intelligence in Healthcare: Foundations, Opportunities and Challenges. Digitalization in Healthcare 2021. [DOI: 10.1007/978-3-030-65896-0_1] [Reference Citation Analysis]
796 Mohapatra S, Satpathy S, Paul D. Data-Driven Symptom Analysis and Location Prediction Model for Clinical Health Data Processing and Knowledgebase Development for COVID-19. Medical Virology: From Pathogenesis to Disease Control 2021. [DOI: 10.1007/978-981-15-7317-0_6] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
797 Kang M, Chikontwe P, Luna M, Hong KS, Ahn JH, Park SH. Mixing-AdaSIN: Constructing a De-biased Dataset Using Adaptive Structural Instance Normalization and Texture Mixing. Predictive Intelligence in Medicine 2021. [DOI: 10.1007/978-3-030-87602-9_4] [Reference Citation Analysis]
798 Awasthi N, Gupta S, Kiran A, Pardasani R. State-of-the-art equipment for rapid and accurate diagnosis of COVID-19. Biomedical Engineering Tools for Management for Patients with COVID-19 2021. [DOI: 10.1016/b978-0-12-824473-9.00012-4] [Reference Citation Analysis]
799 Limketkai BN, Mauldin K, Manitius N, Jalilian L, Salonen BR. The Age of Artificial Intelligence: Use of Digital Technology in Clinical Nutrition. Curr Surg Rep 2021;9:20. [PMID: 34123579 DOI: 10.1007/s40137-021-00297-3] [Cited by in Crossref: 17] [Cited by in F6Publishing: 17] [Article Influence: 8.5] [Reference Citation Analysis]
800 Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix AN, Thys M, Henket M, Canivet G, Mathieu S, Eftaxia E, Lambin P, Tsoutzidis N, Miraglio B, Walsh S, Moutschen M, Louis R, Meunier P, Vos W, Leijenaar RTH, Lovinfosse P. Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19. Diagnostics (Basel) 2020;11:E41. [PMID: 33396587 DOI: 10.3390/diagnostics11010041] [Cited by in Crossref: 17] [Cited by in F6Publishing: 21] [Article Influence: 5.7] [Reference Citation Analysis]
801 Morozov SP, Andreychenko AE, Blokhin IA, Gelezhe PB, Gonchar AP, Nikolaev AE, Pavlov NA, Chernina VY, Gombolevskiy VA. MosMedData: data set of 1110 chest CT scans performed during the COVID-19 epidemic. Digital Diagnostics 2020;1:49-59. [DOI: 10.17816/dd46826] [Cited by in Crossref: 25] [Cited by in F6Publishing: 26] [Article Influence: 8.3] [Reference Citation Analysis]
802 Endargiri S, Laabidi K. Automatic Healthcare Diagnosis and Prediction Assessment based on AI Multi-Classification Algorithm. 2020 16th International Computer Engineering Conference (ICENCO) 2020. [DOI: 10.1109/icenco49778.2020.9357385] [Reference Citation Analysis]
803 Vargo D, Zhu L, Benwell B, Yan Z. Digital technology use during COVID ‐19 pandemic: A rapid review. Human Behav and Emerg Tech 2021;3:13-24. [DOI: 10.1002/hbe2.242] [Cited by in Crossref: 84] [Cited by in F6Publishing: 96] [Article Influence: 28.0] [Reference Citation Analysis]
804 Mathew RP, Jose M, Jayaram V, Joy P, George D, Joseph M, Sleeba T, Toms A. Current status quo on COVID-19 including chest imaging. World J Radiol 2020; 12(12): 272-288 [PMID: 33510852 DOI: 10.4329/wjr.v12.i12.272] [Cited by in CrossRef: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
805 Xie Q, Lu Y, Xie X, Mei N, Xiong Y, Li X, Zhu Y, Xiao A, Yin B. The usage of deep neural network improves distinguishing COVID-19 from other suspected viral pneumonia by clinicians on chest CT: a real-world study. Eur Radiol 2021;31:3864-73. [PMID: 33372243 DOI: 10.1007/s00330-020-07553-7] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
806 Gautret P, Million M, Jarrot PA, Camoin-Jau L, Colson P, Fenollar F, Leone M, La Scola B, Devaux C, Gaubert JY, Mege JL, Vitte J, Melenotte C, Rolain JM, Parola P, Lagier JC, Brouqui P, Raoult D. Natural history of COVID-19 and therapeutic options. Expert Rev Clin Immunol 2020;16:1159-84. [PMID: 33356661 DOI: 10.1080/1744666X.2021.1847640] [Cited by in Crossref: 55] [Cited by in F6Publishing: 42] [Article Influence: 18.3] [Reference Citation Analysis]
807 Gunraj H, Wang L, Wong A. COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images. Front Med (Lausanne) 2020;7:608525. [PMID: 33425953 DOI: 10.3389/fmed.2020.608525] [Cited by in Crossref: 70] [Cited by in F6Publishing: 77] [Article Influence: 23.3] [Reference Citation Analysis]
808 Deng L, Zhong W, Zhao L, He X, Lian Z, Jiang S, Chen CY. Artificial Intelligence-Based Application to Explore Inhibitors of Neurodegenerative Diseases. Front Neurorobot 2020;14:617327. [PMID: 33414713 DOI: 10.3389/fnbot.2020.617327] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
809 Summers RM. Artificial Intelligence of COVID-19 Imaging: A Hammer in Search of a Nail. Radiology 2021;298:E162-4. [PMID: 33350895 DOI: 10.1148/radiol.2020204226] [Cited by in Crossref: 16] [Cited by in F6Publishing: 25] [Article Influence: 5.3] [Reference Citation Analysis]
810 Jin C, Duan Y, Cao Y, Yu J, Xu Z, Chen W, Han X, Liu J, Zhou J, Shi H, Feng J. Voxel-level forecast system for lesion development in patients with COVID-19.. [DOI: 10.1101/2020.12.17.20248377] [Reference Citation Analysis]
811 Sadhukhan P, Ugurlu MT, Hoque MO. Effect of COVID-19 on Lungs: Focusing on Prospective Malignant Phenotypes. Cancers (Basel) 2020;12:E3822. [PMID: 33352869 DOI: 10.3390/cancers12123822] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 6.3] [Reference Citation Analysis]
812 Deep Deb S, Kumar Jha R. COVID-19 detection from chest X-Ray images using ensemble of CNN models. 2020 International Conference on Power, Instrumentation, Control and Computing (PICC) 2020. [DOI: 10.1109/picc51425.2020.9362499] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
813 Kwon YJF, Toussie D, Finkelstein M, Cedillo MA, Maron SZ, Manna S, Voutsinas N, Eber C, Jacobi A, Bernheim A, Gupta YS, Chung MS, Fayad ZA, Glicksberg BS, Oermann EK, Costa AB. Combining Initial Radiographs and Clinical Variables Improves Deep Learning Prognostication in Patients with COVID-19 from the Emergency Department. Radiol Artif Intell 2021;3:e200098. [PMID: 33928257 DOI: 10.1148/ryai.2020200098] [Cited by in Crossref: 18] [Cited by in F6Publishing: 21] [Article Influence: 6.0] [Reference Citation Analysis]
814 Abd-Alrazaq A, Alajlani M, Alhuwail D, Schneider J, Al-Kuwari S, Shah Z, Hamdi M, Househ M. Artificial Intelligence in the Fight Against COVID-19: Scoping Review. J Med Internet Res 2020;22:e20756. [PMID: 33284779 DOI: 10.2196/20756] [Cited by in Crossref: 41] [Cited by in F6Publishing: 45] [Article Influence: 13.7] [Reference Citation Analysis]
815 Nadeem O, Saeed MS, Tahir MA, Mumtaz R. A Survey of Artificial Intelligence and Internet of Things (IoT) based approaches against Covid-19. 2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) 2020. [DOI: 10.1109/honet50430.2020.9322829] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
816 Santana HS, de Souza MRP, Lopes MGM, Souza J, Silva RRO, Palma MSA, Nakano WLV, Lima GAS, Munhoz G, Noriler D, Taranto OP, Silva JL Jr. How chemical engineers can contribute to fight the COVID-19. J Taiwan Inst Chem Eng 2020;116:67-80. [PMID: 33282011 DOI: 10.1016/j.jtice.2020.11.024] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
817 El Helow KR, Salem AM. Are Artificial Intelligence (AI) And Machine Learning (ML) Having An Effective Role In Helping Humanity Address The New Coronavirus Pandemic? WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 2020;17:119-24. [DOI: 10.37394/23208.2020.17.14] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
818 Langer T, Favarato M, Giudici R, Bassi G, Garberi R, Villa F, Gay H, Zeduri A, Bragagnolo S, Molteni A, Beretta A, Corradin M, Moreno M, Vismara C, Perno CF, Buscema M, Grossi E, Fumagalli R. Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data. Scand J Trauma Resusc Emerg Med 2020;28:113. [PMID: 33261629 DOI: 10.1186/s13049-020-00808-8] [Cited by in Crossref: 20] [Cited by in F6Publishing: 24] [Article Influence: 6.7] [Reference Citation Analysis]
819 [DOI: 10.1109/icmla51294.2020.00211] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 3.3] [Reference Citation Analysis]
820 Cai W, Liu T, Xue X, Luo G, Wang X, Shen Y, Fang Q, Sheng J, Chen F, Liang T. CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of COVID-19 Patients. Acad Radiol 2020;27:1665-78. [PMID: 33046370 DOI: 10.1016/j.acra.2020.09.004] [Cited by in Crossref: 45] [Cited by in F6Publishing: 49] [Article Influence: 15.0] [Reference Citation Analysis]
821 Kieu STH, Bade A, Hijazi MHA, Kolivand H. A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions. J Imaging 2020;6:131. [PMID: 34460528 DOI: 10.3390/jimaging6120131] [Cited by in Crossref: 25] [Cited by in F6Publishing: 26] [Article Influence: 8.3] [Reference Citation Analysis]
822 Gross A, Heine G, Schwarz M, Thiemig D, Gläser S, Albrecht T. Structured reporting of chest CT provides high sensitivity and specificity for early diagnosis of COVID-19 in a clinical routine setting. Br J Radiol 2021;94:20200574. [PMID: 33245241 DOI: 10.1259/bjr.20200574] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 2.3] [Reference Citation Analysis]
823 Zhang R, Xiao K, Gu Y, Liu H, Sun X. Whole Genome Identification of Potential G-Quadruplexes and Analysis of the G-Quadruplex Binding Domain for SARS-CoV-2. Front Genet 2020;11:587829. [PMID: 33329730 DOI: 10.3389/fgene.2020.587829] [Cited by in Crossref: 23] [Cited by in F6Publishing: 24] [Article Influence: 7.7] [Reference Citation Analysis]
824 Hefeda MM. CT chest findings in patients infected with COVID-19: review of literature. Egypt J Radiol Nucl Med 2020;51. [DOI: 10.1186/s43055-020-00355-3] [Cited by in Crossref: 13] [Cited by in F6Publishing: 14] [Article Influence: 4.3] [Reference Citation Analysis]
825 Ellison GTH. COVID-19 and the epistemology of epidemiological models at the dawn of AI. Ann Hum Biol 2020;47:506-13. [PMID: 33228409 DOI: 10.1080/03014460.2020.1839132] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
826 Sreepadmanabh M, Sahu AK, Chande A. COVID-19: Advances in diagnostic tools, treatment strategies, and vaccine development. J Biosci 2020;45. [DOI: 10.1007/s12038-020-00114-6] [Cited by in Crossref: 40] [Cited by in F6Publishing: 44] [Article Influence: 13.3] [Reference Citation Analysis]
827 Li J, Long X, Wang X, Fang F, Lv X, Zhang D, Sun Y, Hu S, Lin Z, Xiong N. Radiology indispensable for tracking COVID-19. Diagn Interv Imaging 2021;102:69-75. [PMID: 33281082 DOI: 10.1016/j.diii.2020.11.008] [Cited by in Crossref: 16] [Cited by in F6Publishing: 11] [Article Influence: 5.3] [Reference Citation Analysis]
828 Wehbe RM, Sheng J, Dutta S, Chai S, Dravid A, Barutcu S, Wu Y, Cantrell DR, Xiao N, Allen BD, MacNealy GA, Savas H, Agrawal R, Parekh N, Katsaggelos AK. DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set. Radiology. 2021;299:E167-E176. [PMID: 33231531 DOI: 10.1148/radiol.2020203511] [Cited by in Crossref: 67] [Cited by in F6Publishing: 75] [Article Influence: 22.3] [Reference Citation Analysis]
829 Buonaguro FM, Botti G, Ascierto PA, Pignata S, Ionna F, Delrio P, Petrillo A, Cavalcanti E, Di Bonito M, Perdonà S, De Laurentiis M, Fiore F, Palaia R, Izzo F, D'Auria S, Rossi V, Menegozzo S, Piccirillo M, Celentano E, Cuomo A, Normanno N, Tornesello ML, Saviano R, Barberio D, Buonaguro L, Giannoni G, Muto P, Miscio L, Bianchi AAM; and the INT-Pascale COVID-19 Crisis Unit. The clinical and translational research activities at the INT - IRCCS "Fondazione Pascale" cancer center (Naples, Italy) during the COVID-19 pandemic. Infect Agent Cancer 2020;15:69. [PMID: 33292365 DOI: 10.1186/s13027-020-00330-7] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
830 Roshkovan L, Chatterjee N, Galperin-Aizenberg M, Gupta N, Shah R, Barbosa EM Jr, Simpson S, Cook T, Nachiappan A, Knollmann F, Litt H, Desjardins B, Jha S, Panebianco N, Baston C, Thompson JC, Katz SI. The Role of Imaging in the Management of Suspected or Known COVID-19 Pneumonia. A Multidisciplinary Perspective. Ann Am Thorac Soc. 2020;17:1358-1365. [PMID: 33124905 DOI: 10.1513/annalsats.202006-600fr] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.7] [Reference Citation Analysis]
831 Serte S, Serener A. Early pleural effusion detection from respiratory diseases including COVID-19 via deep learning. 2020 Medical Technologies Congress (TIPTEKNO) 2020. [DOI: 10.1109/tiptekno50054.2020.9299300] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
832 Serener A, Serte S. Deep learning to distinguish COVID-19 from other lung infections, pleural diseases, and lung tumors. 2020 Medical Technologies Congress (TIPTEKNO) 2020. [DOI: 10.1109/tiptekno50054.2020.9299215] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
833 [DOI: 10.1109/tiptekno50054.2020.9299315] [Cited by in Crossref: 16] [Cited by in F6Publishing: 17] [Article Influence: 5.3] [Reference Citation Analysis]
834 von Wedel P, Hagist C. Economic Value of Data and Analytics for Health Care Providers: Hermeneutic Systematic Literature Review. J Med Internet Res 2020;22:e23315. [PMID: 33206056 DOI: 10.2196/23315] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
835 Dastider AG, Rashid Subah M, Sadik F, Mahmud T, Fattah SA. ResCovNet: A Deep Learning-Based Architecture For COVID-19 Detection From Chest CT Scan Images. 2020 IEEE REGION 10 CONFERENCE (TENCON) 2020. [DOI: 10.1109/tencon50793.2020.9293887] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
836 Kang M, Chikontwe P, Luna M, Hong KS, Jang JG, Park J, Shin K, Ahn JH, Park SH. Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia patients: A Deep Learning Perspective.. [DOI: 10.1101/2020.11.13.20231118] [Reference Citation Analysis]
837 Rashid N, Faisal Hossain MA, Ali M, Sukanya MI, Mahmud T, Fattah SA. Transfer Learning Based Method for COVID-19 Detection From Chest X-ray Images. 2020 IEEE REGION 10 CONFERENCE (TENCON) 2020. [DOI: 10.1109/tencon50793.2020.9293850] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
838 Huang Z, Liu X, Wang R, Zhang M, Zeng X, Liu J, Yang Y, Liu X, Zheng H, Liang D, Hu Z. FaNet: fast assessment network for the novel coronavirus (COVID-19) pneumonia based on 3D CT imaging and clinical symptoms. Appl Intell (Dordr) 2020;:1-12. [PMID: 34764567 DOI: 10.1007/s10489-020-01965-0] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 1.7] [Reference Citation Analysis]
839 Wang SH, Nayak DR, Guttery DS, Zhang X, Zhang YD. COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis. Inf Fusion 2021;68:131-48. [PMID: 33519321 DOI: 10.1016/j.inffus.2020.11.005] [Cited by in Crossref: 90] [Cited by in F6Publishing: 101] [Article Influence: 30.0] [Reference Citation Analysis]
840 Özbay E, Altunbey Özbay F. Derin Öğrenme ve Sınıflandırma Yaklaşımları ile BT görüntülerinden Covid-19 Tespiti. DÜMF Mühendislik Dergisi 2020. [DOI: 10.24012/dumf.812810] [Reference Citation Analysis]
841 Shaban WM, Rabie AH, Saleh AI, Abo-Elsoud MA. Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network. Appl Soft Comput 2021;99:106906. [PMID: 33204229 DOI: 10.1016/j.asoc.2020.106906] [Cited by in Crossref: 27] [Cited by in F6Publishing: 30] [Article Influence: 9.0] [Reference Citation Analysis]
842 Liao JL, Chen Y, Huang CQ, He GQ, Du JC, Chen QL. Clinical differences in chest CT characteristics between the progression and remission stages of patients with COVID-19 pneumonia. Int J Clin Pract 2021;75:e13760. [PMID: 33068310 DOI: 10.1111/ijcp.13760] [Reference Citation Analysis]
843 Al-bawi A, Al-kaabi K, Jeryo M, Al-fatlawi A. CCBlock: an effective use of deep learning for automatic diagnosis of COVID-19 using X-ray images. Res Biomed Eng . [DOI: 10.1007/s42600-020-00110-7] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 1.7] [Reference Citation Analysis]
844 Kuchana M, Srivastava A, Das R, Mathew J, Mishra A, Khatter K. AI aiding in diagnosing, tracking recovery of COVID-19 using deep learning on Chest CT scans. Multimed Tools Appl 2020;:1-15. [PMID: 33192159 DOI: 10.1007/s11042-020-10010-8] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 4.0] [Reference Citation Analysis]
845 Zhou T, Lu H, Yang Z, Qiu S, Huo B, Dong Y. The ensemble deep learning model for novel COVID-19 on CT images. Appl Soft Comput 2021;98:106885. [PMID: 33192206 DOI: 10.1016/j.asoc.2020.106885] [Cited by in Crossref: 88] [Cited by in F6Publishing: 95] [Article Influence: 29.3] [Reference Citation Analysis]
846 Chiroma H, Ezugwu AE, Jauro F, Al-garadi MA, Abdullahi IN, Shuib L. Early survey with bibliometric analysis on machine learning approaches in controlling coronavirus.. [DOI: 10.1101/2020.11.04.20225698] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.3] [Reference Citation Analysis]
847 Scholze AR, Melo EC, Major CB, Cruz CFR, de Souza Alcântara LR, Dalcol C, Ferreira Seiva FR, de Fátima Mantovani M, Mattei ÂT, Silveira HS, de Almeida Chuffa LG. Differences and similarities in diagnostic methods and treatments for Coronavirus disease 2019 (COVID-19): a scoping review.. [DOI: 10.1101/2020.10.30.20222950] [Reference Citation Analysis]
848 [DOI: 10.1109/ccci49893.2020.9256562] [Cited by in Crossref: 39] [Cited by in F6Publishing: 43] [Article Influence: 13.0] [Reference Citation Analysis]
849 Hwa SKT, Bade A, Hijazi MHA. Enhanced Canny edge detection for Covid-19 and pneumonia X-Ray images. IOP Conf Ser : Mater Sci Eng 2020;979:012016. [DOI: 10.1088/1757-899x/979/1/012016] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
850 Yang Q, Xu H, Tang X, Hu C, Wang P, Wáng YXJ, Wang Y, Ma G, Zhang B. Medical Imaging Engineering and Technology Branch of the Chinese Society of Biomedical Engineering expert consensus on the application of Emergency Mobile Cabin CT. Quant Imaging Med Surg 2020;10:2191-207. [PMID: 33139998 DOI: 10.21037/qims-20-980] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
851 Shiri I, Akhavanallaf A, Sanaat A, Salimi Y, Askari D, Mansouri Z, Shayesteh SP, Hasanian M, Rezaei-kalantari K, Salahshour A, Sandoughdaran S, Abdollahi H, Arabi H, Zaidi H. Deep Residual Neural Network-based Standard CT Estimation from Ultra-Low Dose CT Imaging for COVID-19 Patients. 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) 2020. [DOI: 10.1109/nss/mic42677.2020.9507847] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
852 Ahsan MM, Gupta KD, Islam MM, Sen S, Rahman ML, Shakhawat Hossain M. COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities. MAKE 2020;2:490-504. [DOI: 10.3390/make2040027] [Cited by in Crossref: 26] [Cited by in F6Publishing: 29] [Article Influence: 8.7] [Reference Citation Analysis]
853 Izquierdo JL, Ancochea J, Soriano JB; Savana COVID-19 Research Group. Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing. J Med Internet Res 2020;22:e21801. [PMID: 33090964 DOI: 10.2196/21801] [Cited by in Crossref: 54] [Cited by in F6Publishing: 60] [Article Influence: 18.0] [Reference Citation Analysis]
854 Scott IA, Coiera EW. Can AI help in the fight against COVID-19? Med J Aust 2020;213:439-441.e2. [PMID: 33111999 DOI: 10.5694/mja2.50821] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
855 Chen J, See KC. Artificial Intelligence for COVID-19: Rapid Review. J Med Internet Res. 2020;22:e21476. [PMID: 32946413 DOI: 10.2196/21476] [Cited by in Crossref: 49] [Cited by in F6Publishing: 55] [Article Influence: 16.3] [Reference Citation Analysis]
856 Kang B, Guo J, Meng X. Rapid Implementation of COVID-19 AI Assisted Diagnosis System Based on Supercomputing Platform. 2020 5th International Conference on Universal Village (UV) 2020. [DOI: 10.1109/uv50937.2020.9426227] [Reference Citation Analysis]
857 Irmak E. Implementation of convolutional neural network approach for COVID-19 disease detection. Physiol Genomics 2020;52:590-601. [PMID: 33094700 DOI: 10.1152/physiolgenomics.00084.2020] [Cited by in Crossref: 13] [Cited by in F6Publishing: 15] [Article Influence: 4.3] [Reference Citation Analysis]
858 [DOI: 10.1109/ismsit50672.2020.9254970] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 3.0] [Reference Citation Analysis]
859 Cabitza F, Campagner A, Ferrari D, Di Resta C, Ceriotti D, Sabetta E, Colombini A, De Vecchi E, Banfi G, Locatelli M, Carobene A. Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests. Clin Chem Lab Med 2020;59:421-31. [PMID: 33079698 DOI: 10.1515/cclm-2020-1294] [Cited by in Crossref: 48] [Cited by in F6Publishing: 61] [Article Influence: 16.0] [Reference Citation Analysis]
860 Cai Q, Du SY, Gao S, Huang GL, Zhang Z, Li S, Wang X, Li PL, Lv P, Hou G, Zhang LN. A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients. BMC Med Imaging 2020;20:118. [PMID: 33081700 DOI: 10.1186/s12880-020-00521-z] [Cited by in Crossref: 16] [Cited by in F6Publishing: 18] [Article Influence: 5.3] [Reference Citation Analysis]
861 Shen TS, Driscoll DA, Islam W, Bovonratwet P, Haas SB, Su EP. Modern Internet Search Analytics and Total Joint Arthroplasty: What Are Patients Asking and Reading Online? J Arthroplasty 2021;36:1224-31. [PMID: 33162279 DOI: 10.1016/j.arth.2020.10.024] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 4.0] [Reference Citation Analysis]
862 Trivedi A, Ortiz A, Desbiens J, Robinson C, Blazes M, Gupta S, Dodhia R, Bhatraju P, Liles WC, Lee A, Ferres JML. Effective Deep Learning Approaches for Predicting COVID-19 Outcomes from Chest Computed Tomography Volumes.. [DOI: 10.1101/2020.10.15.20213462] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
863 [DOI: 10.1145/3340531.3412179] [Cited by in Crossref: 9] [Cited by in F6Publis