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For: Walsh SLF, Calandriello L, Silva M, Sverzellati N. Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. The Lancet Respiratory Medicine 2018;6:837-45. [DOI: 10.1016/s2213-2600(18)30286-8] [Cited by in Crossref: 161] [Cited by in F6Publishing: 123] [Article Influence: 32.2] [Reference Citation Analysis]
Number Citing Articles
1 Sollini M, Loiacono D, Volpe D, Levra AG, Lomeo E, Giacomello E, Kirienko M, Chiti A, Lanzi P. Artificial Intelligence in Diagnostic Imaging. Radiology‐Nuclear Medicine Diagnostic Imaging 2023. [DOI: 10.1002/9781119603627.ch31] [Reference Citation Analysis]
2 Mehrpour O, Nakhaee S, Saeedi F, Valizade B, Lotfi E, Nawaz MH. Utility of artificial intelligence to identify antihyperglycemic agents poisoning in the USA: introducing a practical web application using National Poison Data System (NPDS). Environ Sci Pollut Res Int 2023. [PMID: 36973614 DOI: 10.1007/s11356-023-26605-1] [Reference Citation Analysis]
3 Wen J, Liu D, Wu Q, Zhao L, Iao WC, Lin H. Retinal image‐based artificial intelligence in detecting and predicting kidney diseases: Current advances and future perspectives. VIEW 2023. [DOI: 10.1002/viw.20220070] [Reference Citation Analysis]
4 Haubold J, Zeng K, Farhand S, Stalke S, Steinberg H, Bos D, Meetschen M, Kureishi A, Zensen S, Goeser T, Maier S, Forsting M, Nensa F. AI co-pilot: content-based image retrieval for the reading of rare diseases in chest CT. Sci Rep 2023;13:4336. [PMID: 36928759 DOI: 10.1038/s41598-023-29949-3] [Reference Citation Analysis]
5 Pugashetti JV, Khanna D, Kazerooni EA, Oldham J. Clinically Relevant Biomarkers in Connective Tissue Disease-Associated Interstitial Lung Disease. Immunology and Allergy Clinics of North America 2023. [DOI: 10.1016/j.iac.2023.01.012] [Reference Citation Analysis]
6 Hochhegger B, Pasini R, Roncally Carvalho A, Rodrigues R, Altmayer S, Kayat Bittencourt L, Marchiori E, Forghani R. Artificial Intelligence for Cardiothoracic Imaging: Overview of Current and Emerging Applications. Seminars in Roentgenology 2023. [DOI: 10.1053/j.ro.2023.02.001] [Reference Citation Analysis]
7 Yu W, Zhou H, Choi Y, Goldin JG, Teng P, Wong WK, McNitt-Gray MF, Brown MS, Kim GHJ. Multi-scale, domain knowledge-guided attention + random forest: a two-stage deep learning-based multi-scale guided attention models to diagnose idiopathic pulmonary fibrosis from computed tomography images. Med Phys 2023;50:894-905. [PMID: 36254789 DOI: 10.1002/mp.16053] [Reference Citation Analysis]
8 Nishikiori H, Kuronuma K, Hirota K, Yama N, Suzuki T, Onodera M, Onodera K, Ikeda K, Mori Y, Asai Y, Takagi Y, Honda S, Ohnishi H, Hatakenaka M, Takahashi H, Chiba H. Deep-learning algorithm to detect fibrosing interstitial lung disease on chest radiographs. Eur Respir J 2023;61. [PMID: 36202411 DOI: 10.1183/13993003.02269-2021] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 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]
10 Karampitsakos T, Sotiropoulou V, Katsaras M, Tsiri P, Georgakopoulou VE, Papanikolaou IC, Bibaki E, Tomos I, Lambiri I, Papaioannou O, Zarkadi E, Antonakis E, Pandi A, Malakounidou E, Sampsonas F, Makrodimitri S, Chrysikos S, Hillas G, Dimakou K, Tzanakis N, Sipsas NV, Antoniou K, Tzouvelekis A. Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model. Front Med (Lausanne) 2022;9:1083264. [PMID: 36733935 DOI: 10.3389/fmed.2022.1083264] [Reference Citation Analysis]
11 Barnes H, Humphries SM, George PM, Assayag D, Glaspole I, Mackintosh JA, Corte TJ, Glassberg M, Johannson KA, Calandriello L, Felder F, Wells A, Walsh S. Machine learning in radiology: the new frontier in interstitial lung diseases. Lancet Digit Health 2023;5:e41-50. [PMID: 36517410 DOI: 10.1016/S2589-7500(22)00230-8] [Reference Citation Analysis]
12 Raju N, Augustine DP, Anita HB. A Novel Deep Learning Approach for Identifying Interstitial Lung Diseases from HRCT Images. SN COMPUT SCI 2022;4:132. [DOI: 10.1007/s42979-022-01579-y] [Reference Citation Analysis]
13 Choe J, Lee SM, Hwang HJ, Lee SM, Yun J, Kim N, Seo JB. Artificial Intelligence in Lung Imaging. Semin Respir Crit Care Med 2022;43:946-60. [PMID: 36174647 DOI: 10.1055/s-0042-1755571] [Reference Citation Analysis]
14 Mekov E, Ilieva V. Machine learning in lung transplantation: Where are we? Presse Med 2022;51:104140. [PMID: 36252820 DOI: 10.1016/j.lpm.2022.104140] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Humphries SM, Mackintosh JA, Jo HE, Walsh SLF, Silva M, Calandriello L, Chapman S, Ellis S, Glaspole I, Goh N, Grainge C, Hopkins PMA, Keir GJ, Moodley Y, Reynolds PN, Walters EH, Baraghoshi D, Wells AU, Lynch DA, Corte TJ. Quantitative computed tomography predicts outcomes in idiopathic pulmonary fibrosis. Respirology 2022;27:1045-53. [PMID: 35875881 DOI: 10.1111/resp.14333] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
16 Gabryś HS, Gote-schniering J, Brunner M, Bogowicz M, Blüthgen C, Frauenfelder T, Guckenberger M, Maurer B, Tanadini-lang S. Transferability of radiomic signatures from experimental to human interstitial lung disease. Front Med 2022;9. [DOI: 10.3389/fmed.2022.988927] [Reference Citation Analysis]
17 Vincenzi E, Fantazzini A, Basso C, Barla A, Odone F, Leo L, Mecozzi L, Mambrini M, Ferrini E, Sverzellati N, Stellari FF. A fully automated deep learning pipeline for micro-CT-imaging-based densitometry of lung fibrosis murine models. Respir Res 2022;23:308. [DOI: 10.1186/s12931-022-02236-x] [Reference Citation Analysis]
18 Schwartz AV, Lee AN, Theilmann RJ, George UZ. Spatial Heterogeneity of Excess Lung Fluid in Cystic Fibrosis: Generalized, Localized Diffuse, and Localized Presentations. Applied Sciences 2022;12:10647. [DOI: 10.3390/app122010647] [Reference Citation Analysis]
19 Zhang S, Yuan G, Ijaz MF. Deep Transfer Learning for COVID-19 Detection and Lesion Recognition Using Chest CT Images. Computational and Mathematical Methods in Medicine 2022;2022:1-16. [DOI: 10.1155/2022/4509394] [Reference Citation Analysis]
20 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]
21 Jesudasen SJ, Montesi SB. Beyond What Meets the Eye: Artificial Intelligence in the Diagnosis of Idiopathic Pulmonary Fibrosis. Chest 2022;162:734-5. [PMID: 36210098 DOI: 10.1016/j.chest.2022.04.152] [Reference Citation Analysis]
22 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]
23 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]
24 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]
25 Hoang-thi T, Chassagnon G, Tran H, Le-dong N, Dinh-xuan AT, Revel M. How Artificial Intelligence in Imaging Can Better Serve Patients with Bronchial and Parenchymal Lung Diseases? JPM 2022;12:1429. [DOI: 10.3390/jpm12091429] [Reference Citation Analysis]
26 Guiot J, Maes N, Winandy M, Henket M, Ernst B, Thys M, Frix A, Morimont P, Rousseau A, Canivet P, Louis R, Misset B, Meunier P, Charbonnier J, Lambermont B. Automatized lung disease quantification in patients with COVID-19 as a predictive tool to assess hospitalization severity. Front Med 2022;9. [DOI: 10.3389/fmed.2022.930055] [Reference Citation Analysis]
27 Wang C, Ma J, Zhang S, Shao J, Wang Y, Zhou HY, Song L, Zheng J, Yu Y, Li W. Development and validation of an abnormality-derived deep-learning diagnostic system for major respiratory diseases. NPJ Digit Med 2022;5:124. [PMID: 35999467 DOI: 10.1038/s41746-022-00648-z] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
28 Vliegenthart R, Fouras A, Jacobs C, Papanikolaou N. Innovations in thoracic imaging: CT, radiomics, AI and x-ray velocimetry. Respirology 2022. [PMID: 35965430 DOI: 10.1111/resp.14344] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
29 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]
30 Wells AU, Walsh SLF. Quantitative computed tomography and machine learning: recent data in fibrotic interstitial lung disease and potential role in pulmonary sarcoidosis. Curr Opin Pulm Med 2022. [PMID: 35861463 DOI: 10.1097/MCP.0000000000000902] [Reference Citation Analysis]
31 Jeong MH, Han H, Lagares D, Im H. Recent Advances in Molecular Diagnosis of Pulmonary Fibrosis for Precision Medicine. ACS Pharmacol Transl Sci . [DOI: 10.1021/acsptsci.2c00028] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
32 Yamada H, Ohmori R, Okada N, Nakamura S, Kagawa K, Fujii S, Miki H, Ishizawa K, Abe M, Sato Y. A machine learning model using SNPs obtained from a genome-wide association study predicts the onset of vincristine-induced peripheral neuropathy. Pharmacogenomics J 2022;22:241-246. [DOI: 10.1038/s41397-022-00282-8] [Reference Citation Analysis]
33 Refaee T, Salahuddin Z, Frix A, Yan C, Wu G, Woodruff HC, Gietema H, Meunier P, Louis R, Guiot J, Lambin P. Diagnosis of Idiopathic Pulmonary Fibrosis in High-Resolution Computed Tomography Scans Using a Combination of Handcrafted Radiomics and Deep Learning. Front Med 2022;9:915243. [DOI: 10.3389/fmed.2022.915243] [Reference Citation Analysis]
34 Furukawa T, Oyama S, Yokota H, Kondoh Y, Kataoka K, Johkoh T, Fukuoka J, Hashimoto N, Sakamoto K, Shiratori Y, Hasegawa Y. A comprehensible machine learning tool to differentially diagnose idiopathic pulmonary fibrosis from other chronic interstitial lung diseases. Respirology 2022. [PMID: 35697345 DOI: 10.1111/resp.14310] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
35 Mambrini M, Mecozzi L, Ferrini E, Leo L, Bernardi D, Grandi A, Sverzellati N, Ruffini L, Silva M, Stellari FF. The importance of routine quality control for reproducible pulmonary measurements by in vivo micro-CT. Sci Rep 2022;12:9695. [PMID: 35690601 DOI: 10.1038/s41598-022-13477-7] [Reference Citation Analysis]
36 Koo CW, Williams JM, Liu G, Panda A, Patel PP, Frota Lima LMM, Karwoski RA, Moua T, Larson NB, Bratt A. Quantitative CT and machine learning classification of fibrotic interstitial lung diseases. Eur Radiol 2022. [PMID: 35678861 DOI: 10.1007/s00330-022-08875-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
37 Yu AC, Mohajer B, Eng J. External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review. Radiol Artif Intell 2022;4:e210064. [PMID: 35652114 DOI: 10.1148/ryai.210064] [Cited by in Crossref: 3] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
38 Zhang L, Jiang B, Wisselink HJ, Vliegenthart R, Xie X. COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images. Br J Radiol 2022;95:20210637. [PMID: 35143286 DOI: 10.1259/bjr.20210637] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
39 Raghu G, Remy-Jardin M, Richeldi L, Thomson CC, Inoue Y, Johkoh T, Kreuter M, Lynch DA, Maher TM, Martinez FJ, Molina-Molina M, Myers JL, Nicholson AG, Ryerson CJ, Strek ME, Troy LK, Wijsenbeek M, Mammen MJ, Hossain T, Bissell BD, Herman DD, Hon SM, Kheir F, Khor YH, Macrea M, Antoniou KM, Bouros D, Buendia-Roldan I, Caro F, Crestani B, Ho L, Morisset J, Olson AL, Podolanczuk A, Poletti V, Selman M, Ewing T, Jones S, Knight SL, Ghazipura M, Wilson KC. Idiopathic Pulmonary Fibrosis (an Update) and Progressive Pulmonary Fibrosis in Adults: An Official ATS/ERS/JRS/ALAT Clinical Practice Guideline. Am J Respir Crit Care Med 2022;205:e18-47. [PMID: 35486072 DOI: 10.1164/rccm.202202-0399ST] [Cited by in Crossref: 140] [Cited by in F6Publishing: 127] [Article Influence: 140.0] [Reference Citation Analysis]
40 [DOI: 10.1109/icsp54964.2022.9778477] [Reference Citation Analysis]
41 Bratt A, Williams JM, Liu G, Panda A, Patel PP, Walkoff L, Sykes AG, Tandon YK, Francois CJ, Blezek DJ, Larson NB, Erickson BJ, Yi ES, Moua T, Koo CW. Predicting Usual Interstitial Pneumonia Histopathology From Chest CT Imaging With Deep Learning. Chest 2022. [DOI: 10.1016/j.chest.2022.03.044] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
42 Yang K, Yang Y, Kang Y, Liang Z, Wang F, Li Q, Xu J, Tang G, Chen R. The value of radiomic features in chronic obstructive pulmonary disease assessment: a prospective study. Clinical Radiology 2022. [DOI: 10.1016/j.crad.2022.02.015] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
43 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]
44 Ye Q, Gao Y, Ding W, Niu Z, Wang C, Jiang Y, Wang M, Fang EF, Menpes-Smith W, Xia J, Yang G. Robust weakly supervised learning for COVID-19 recognition using multi-center CT images. Appl Soft Comput 2022;116:108291. [PMID: 34934410 DOI: 10.1016/j.asoc.2021.108291] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 9.0] [Reference Citation Analysis]
45 Aliboni L, Dias OM, Pennati F, Baldi BG, Sawamura MVY, Chate RC, Carvalho CRR, de Albuquerque ALP, Aliverti A. Quantitative CT Analysis in Chronic Hypersensitivity Pneumonitis: A Convolutional Neural Network Approach. Acad Radiol 2022;29 Suppl 2:S31-40. [PMID: 33168391 DOI: 10.1016/j.acra.2020.10.009] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
46 Soffer S, Morgenthau AS, Shimon O, Barash Y, Konen E, Glicksberg BS, Klang E. Artificial Intelligence for Interstitial Lung Disease Analysis on Chest Computed Tomography: A Systematic Review. Acad Radiol 2022;29 Suppl 2:S226-35. [PMID: 34219012 DOI: 10.1016/j.acra.2021.05.014] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
47 Yadav A, Saxena R, Kumar A, Walia TS, Zaguia A, Kamal SMM, Gupta SK. FVC-NET: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning. Computational Intelligence and Neuroscience 2022;2022:1-12. [DOI: 10.1155/2022/2832400] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
48 Khanna D, Distler O, Cottin V, Brown KK, Chung L, Goldin JG, Matteson EL, Kazerooni EA, Walsh SL, Mcnitt-gray M, Maher TM. Diagnosis and monitoring of systemic sclerosis-associated interstitial lung disease using high-resolution computed tomography. Journal of Scleroderma and Related Disorders. [DOI: 10.1177/23971983211064463] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
49 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]
50 Savadjiev P, Gallix B, Rezanejad M, Bhatnagar S, Semionov A, Siddiqi K, Forghani R, Reinhold C, Eidelman DH, Dandurand RJ. Improved Detection of Chronic Obstructive Pulmonary Disease at Chest CT Using the Mean Curvature of Isophotes. Radiol Artif Intell 2022;4:e210105. [PMID: 35146436 DOI: 10.1148/ryai.210105] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
51 Chauhan NK, Asfahan S, Dutt N, Jalandra RN. Artificial intelligence in the practice of pulmonology: The future is now. Lung India 2022;39:1-2. [PMID: 34975044 DOI: 10.4103/lungindia.lungindia_692_21] [Reference Citation Analysis]
52 Ono S, Goto T. Introduction to supervised machine learning in clinical epidemiology. Ann Clin Epidemiol 2022;4:63-71. [DOI: 10.37737/ace.22009] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
53 J PM, Bhushan V, Hr SK, G SK, J S. Prediction of Pulmonary Fibrosis Progression using CNN and Regression. 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) 2021. [DOI: 10.1109/icac3n53548.2021.9725730] [Reference Citation Analysis]
54 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]
55 Wong A, Lu J, Dorfman A, McInnis P, Famouri M, Manary D, Lee JRH, Lynch M. Fibrosis-Net: A Tailored Deep Convolutional Neural Network Design for Prediction of Pulmonary Fibrosis Progression From Chest CT Images. Front Artif Intell 2021;4:764047. [PMID: 34805974 DOI: 10.3389/frai.2021.764047] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
56 Pogarell T, Bayerl N, Wetzl M, Roth JP, Speier C, Cavallaro A, Uder M, Dankerl P. Evaluation of a Novel Content-Based Image Retrieval System for the Differentiation of Interstitial Lung Diseases in CT Examinations. Diagnostics (Basel) 2021;11:2114. [PMID: 34829461 DOI: 10.3390/diagnostics11112114] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
57 Aliboni L, Dias OM, Baldi BG, Sawamura MVY, Chate RC, Carvalho CRR, de Albuquerque ALP, Aliverti A, Pennati F. A Convolutional Neural Network Approach to Quantify Lung Disease Progression in Patients with Fibrotic Hypersensitivity Pneumonitis (HP). Acad Radiol 2021:S1076-6332(21)00463-3. [PMID: 34794883 DOI: 10.1016/j.acra.2021.10.005] [Reference Citation Analysis]
58 Lee S, Summers RM. Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen. Radiol Clin North Am 2021;59:987-1002. [PMID: 34689882 DOI: 10.1016/j.rcl.2021.07.001] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
59 Ali S, Hussain A, Aich S, Park MS, Chung MP, Jeong SH, Song JW, Lee JH, Kim HC. A Soft Voting Ensemble-Based Model for the Early Prediction of Idiopathic Pulmonary Fibrosis (IPF) Disease Severity in Lungs Disease Patients. Life (Basel) 2021;11. [PMID: 34685461 DOI: 10.3390/life11101092] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
60 Choe J, Hwang HJ, Seo JB, Lee SM, Yun J, Kim MJ, Jeong J, Lee Y, Jin K, Park R, Kim J, Jeon H, Kim N, Yi J, Yu D, Kim B. Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT. Radiology 2021;:204164. [PMID: 34636634 DOI: 10.1148/radiol.2021204164] [Cited by in Crossref: 9] [Cited by in F6Publishing: 11] [Article Influence: 4.5] [Reference Citation Analysis]
61 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]
62 Kise Y, Kuwada C, Ariji Y, Naitoh M, Ariji E. Preliminary Study on the Diagnostic Performance of a Deep Learning System for Submandibular Gland Inflammation Using Ultrasonography Images. J Clin Med 2021;10:4508. [PMID: 34640523 DOI: 10.3390/jcm10194508] [Reference Citation Analysis]
63 Perkonigg M, Hofmanninger J, Herold CJ, Brink JA, Pianykh O, Prosch H, Langs G. Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging. Nat Commun 2021;12:5678. [PMID: 34584080 DOI: 10.1038/s41467-021-25858-z] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
64 Jiang M, Li Y, Jiang C, Zhao L, Zhang X, Lipsky PE. Machine Learning in Rheumatic Diseases. Clin Rev Allergy Immunol 2021;60:96-110. [PMID: 32681407 DOI: 10.1007/s12016-020-08805-6] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
65 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]
66 Handa T, Tanizawa K, Oguma T, Uozumi R, Watanabe K, Tanabe N, Niwamoto T, Shima H, Mori R, Nobashi TW, Sakamoto R, Kubo T, Kurosaki A, Kishi K, Nakamoto Y, Hirai T. Novel Artificial Intelligence-based Technology for Chest Computed Tomography Analysis of Idiopathic Pulmonary Fibrosis. Ann Am Thorac Soc 2021. [PMID: 34410886 DOI: 10.1513/AnnalsATS.202101-044OC] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
67 Shaish H, Ahmed FS, Lederer D, D'Souza B, Armenta P, Salvatore M, Saqi A, Huang S, Jambawalikar S, Mutasa S. Deep Learning of Computed Tomography Virtual Wedge Resection for Prediction of Histologic Usual Interstitial Pneumonitis. Ann Am Thorac Soc 2021;18:51-9. [PMID: 32857594 DOI: 10.1513/AnnalsATS.202001-068OC] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
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