This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2452) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Outbreak and Pandemic Preparedness and Management (1727) Machine Learning (1465) Artificial Intelligence (1371)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42717, first published .
Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study

Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study

Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study

Authors of this article:

Hyun Woo Lee1, 2 Author Orcid Image ;   Hyun Jun Yang2 Author Orcid Image ;   Hyungjin Kim2, 3 Author Orcid Image ;   Ue-Hwan Kim4 Author Orcid Image ;   Dong Hyun Kim2, 5 Author Orcid Image ;   Soon Ho Yoon2, 3 Author Orcid Image ;   Soo-Youn Ham6 Author Orcid Image ;   Bo Da Nam7 Author Orcid Image ;   Kum Ju Chae8 Author Orcid Image ;   Dabee Lee9 Author Orcid Image ;   Jin Young Yoo10 Author Orcid Image ;   So Hyeon Bak11 Author Orcid Image ;   Jin Young Kim12 Author Orcid Image ;   Jin Hwan Kim13 Author Orcid Image ;   Ki Beom Kim14 Author Orcid Image ;   Jung Im Jung15 Author Orcid Image ;   Jae-Kwang Lim16 Author Orcid Image ;   Jong Eun Lee17 Author Orcid Image ;   Myung Jin Chung18 Author Orcid Image ;   Young Kyung Lee19 Author Orcid Image ;   Young Seon Kim20 Author Orcid Image ;   Sang Min Lee21 Author Orcid Image ;   Woocheol Kwon22 Author Orcid Image ;   Chang Min Park2, 3 Author Orcid Image ;   Yun-Hyeon Kim17 Author Orcid Image ;   Yeon Joo Jeong23 Author Orcid Image ;   Kwang Nam Jin2, 5 Author Orcid Image ;   Jin Mo Goo2, 3 Author Orcid Image
Article Authors Cited by (6) Tweetations (8) Metrics

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  2. Zhou Y, Feng J, Mei S, Tang R, Xing S, Qin S, Zhang Z, Xu Q, Gao Y, He Z. A deep learning model for predicting COVID-19 ARDS in critically ill patients. Frontiers in Medicine 2023;10 View
  3. Grenier P, Brun A, Mellot F. Apport de l’intelligence artificielle dans le post-traitement de l’imagerie thoracique. Revue des Maladies Respiratoires 2023 View
  4. Lee J, Jin K, Hong H, Jeong Y, Yoon S. Effectiveness of COVID-19 Vaccines Over Time Against Clinical and Radiologic Outcomes Related to Severe SARS-CoV-2 Infection. Radiology 2024;310(1) View
  5. Shetty S, Mubarak A, R David L, Al Jouhari M, Talaat W, Al-Rawi N, AlKawas S, Shetty S, Uzun Ozsahin D. The Application of Mask Region-Based Convolutional Neural Networks in the Detection of Nasal Septal Deviation Using Cone Beam Computed Tomography Images: Proof-of-Concept Study. JMIR Formative Research 2024;8:e57335 View