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For: Feng Y, Wang Y, Zeng C, Mao H. Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease. Int J Med Sci 2021;18:2871-89. [PMID: 34220314 DOI: 10.7150/ijms.58191] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 8.0] [Reference Citation Analysis]
Number Citing Articles
1 Alvarado E, Grágeda N, Luzanto A, Mahu R, Wuth J, Mendoza L, Yoma NB. Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone. Sensors (Basel) 2023;23. [PMID: 36904646 DOI: 10.3390/s23052441] [Reference Citation Analysis]
2 Iqbal MA, Devarajan K, Ahmed SM. Optimal convolutional neural network classifier for asthma disease detection using speech signals. International Journal of Healthcare Management 2023. [DOI: 10.1080/20479700.2023.2173774] [Reference Citation Analysis]
3 Zhang G, Luo L, Zhang L, Liu Z. Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence. Diagnostics (Basel) 2023;13. [PMID: 36766460 DOI: 10.3390/diagnostics13030357] [Reference Citation Analysis]
4 Mahdavi MMB, Arabfard M, Rafati M, Ghanei M. A Computer-based Analysis for Identification and Quantification of Small Airway Disease in Lung Computed Tomography Images: A Comprehensive Review for Radiologists. J Thorac Imaging 2023;38:W1-W18. [PMID: 36206107 DOI: 10.1097/RTI.0000000000000683] [Reference Citation Analysis]
5 Bui HM, Ha MH, Pham HG, Dao TP, Nguyen TT, Nguyen ML, Vuong NT, Hoang XHT, Do LT, Dao TX, Le CQ. Predicting the risk of osteoporosis in older Vietnamese women using machine learning approaches. Sci Rep 2022;12:20160. [PMID: 36418408 DOI: 10.1038/s41598-022-24181-x] [Reference Citation Analysis]
6 Saleh M, Cevik M. Diagnosis of respiratory diseases for children using machine learning. 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) 2022. [DOI: 10.1109/ismsit56059.2022.9932662] [Reference Citation Analysis]
7 Rani A, Sehrawat H. Role Of Machine Learning and Random Forest in Accuracy Enhancement During Asthma Prediction. 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) 2022. [DOI: 10.1109/icrito56286.2022.9965149] [Reference Citation Analysis]
8 Masanam HB, Perumal G, Krishnan S, Singh SK, Jha NK, Chellappan DK, Dua K, Gupta PK, Narasimhan AK. Advances and opportunities in nanoimaging agents for the diagnosis of inflammatory lung diseases. Nanomedicine (Lond) 2022;17:1981-2005. [PMID: 36695290 DOI: 10.2217/nnm-2021-0427] [Reference Citation Analysis]
9 Jayamini WKD, Mirza F, Naeem MA, Chan AHY. State of Asthma-Related Hospital Admissions in New Zealand and Predicting Length of Stay Using Machine Learning. Applied Sciences 2022;12:9890. [DOI: 10.3390/app12199890] [Reference Citation Analysis]
10 Joumaa H, Sigogne R, Maravic M, Perray L, Bourdin A, Roche N. Artificial intelligence to differentiate asthma from COPD in medico-administrative databases. BMC Pulm Med 2022;22:357. [PMID: 36127649 DOI: 10.1186/s12890-022-02144-2] [Reference Citation Analysis]
11 Mo A, Gui E, Fletcher RR. Use of Voluntary Cough Sounds and Deep Learning for Pulmonary Disease Screening in Low-Resource Areas. 2022 IEEE Global Humanitarian Technology Conference (GHTC) 2022. [DOI: 10.1109/ghtc55712.2022.9911027] [Reference Citation Analysis]
12 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]
13 El-badawy IM, Omar Z, Singh OP. An Effective Machine Learning Approach for Classifying Artefact-Free and Distorted Capnogram Segments Using Simple Time-Domain Features. IEEE Access 2022;10:8767-78. [DOI: 10.1109/access.2022.3143617] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Watson A, Wilkinson TMA. Digital healthcare in COPD management: a narrative review on the advantages, pitfalls, and need for further research. Ther Adv Respir Dis 2022;16:17534666221075493. [PMID: 35234090 DOI: 10.1177/17534666221075493] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
15 Chien TY, Ting HW, Chen CF, Yang CZ, Chen CY. A Clinical Decision Support System for Diabetes Patients with Deep Learning: Experience of a Taiwan Medical Center. Int J Med Sci 2022;19:1049-55. [PMID: 35813300 DOI: 10.7150/ijms.71341] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Liao KM, Liu CF, Chen CJ, Shen YT. Machine Learning Approaches for Predicting Acute Respiratory Failure, Ventilator Dependence, and Mortality in Chronic Obstructive Pulmonary Disease. Diagnostics (Basel) 2021;11:2396. [PMID: 34943632 DOI: 10.3390/diagnostics11122396] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]