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

Artificial Intelligence (1350) Clinical Information and Decision Making (1388) Decision Support for Health Professionals (1173) Clinical Informatics (1009) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1388) Infectious Diseases (non-STD/STI) (1563) Hygiene and Infection Prevention (313) Nursing and Care for Patients with Infectious or Chronic Conditions (21) Machine Learning (42) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (33)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58413, first published .
Development and Validation of Deep Learning–Based Infectivity Prediction in Pulmonary Tuberculosis Through Chest Radiography: Retrospective Study

Development and Validation of Deep Learning–Based Infectivity Prediction in Pulmonary Tuberculosis Through Chest Radiography: Retrospective Study

Development and Validation of Deep Learning–Based Infectivity Prediction in Pulmonary Tuberculosis Through Chest Radiography: Retrospective Study

Authors of this article:

Wou young Chung1 Author Orcid Image ;   Jinsik Yoon2 Author Orcid Image ;   Dukyong Yoon3, 4, 5 Author Orcid Image ;   Songsoo Kim3 Author Orcid Image ;   Yujeong Kim3 Author Orcid Image ;   Ji Eun Park1 Author Orcid Image ;   Young Ae Kang6 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Wou young Chung   1 * , MD, MS ;   Jinsik Yoon   2 * , BS ;   Dukyong Yoon   3, 4, 5 * , MD, PhD ;   Songsoo Kim   3 , MD, MS ;   Yujeong Kim   3 , PhD ;   Ji Eun Park   1 , MD, PhD ;   Young Ae Kang   6 , MD, PhD

1 Department of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon, Republic of Korea

2 Department of Integrative Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea

3 Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea

4 Institute for Innovation in Digital Healthcare, Severance Hospital, Seoul, Republic of Korea

5 Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, Republic of Korea

6 Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea

*these authors contributed equally

Corresponding Author:

  • Young Ae Kang, MD, PhD
  • Department of Internal Medicine
  • Yonsei University College of Medicine
  • 50-1 Yonsei-ro, Seodaemun-gu
  • Seoul, 03722
  • Republic of Korea
  • Phone: 82 2 2228 1954
  • Email: mdkang@yuhs.ac