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

Digital Health Reviews (1729) Decision Support for Health Professionals (1548) mHealth for Screening (297) Clinical Information and Decision Making (1927) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1737) Machine Learning (2081) Surveillance Systems (514) Infectious Diseases (non-STD/STI) (1727) Artificial Intelligence (2144)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69068, first published .
Diagnostic Performance of Artificial Intelligence–Based Methods for Tuberculosis Detection: Systematic Review

Diagnostic Performance of Artificial Intelligence–Based Methods for Tuberculosis Detection: Systematic Review

Diagnostic Performance of Artificial Intelligence–Based Methods for Tuberculosis Detection: Systematic Review

Authors of this article:

Seng Hansun1, 2 Author Orcid Image ;   Ahmadreza Argha3, 4, 5 Author Orcid Image ;   Ivan Bakhshayeshi3, 6 Author Orcid Image ;   Arya Wicaksana7 Author Orcid Image ;   Hamid Alinejad-Rokny4, 5, 6 Author Orcid Image ;   Greg J Fox8 Author Orcid Image ;   Siaw-Teng Liaw9 Author Orcid Image ;   Branko G Celler10 Author Orcid Image ;   Guy B Marks1, 2, 11 Author Orcid Image
Article Authors Cited by (8) Tweetations (3) Metrics

Journals

  1. Pavel I, Ciocoiu I. Tuberculosis Detection from Cough Recordings Using Bag-of-Words Classifiers. Sensors 2025;25(19):6133 View
  2. Niranjane V, Kaur L, More A, Taushif Khan S, Niranjane P, Nikhade P. Intrinsic motivation-based exploration for enhancing tuberculosis lesion discovery in sparse annotation chest X-ray datasets. Indian Journal of Tuberculosis 2025 View
  3. Daradkeh A, Alawyia B, Ballas H, Spernovasilis N, Alon-Ellenbogen D. Strategies for Tuberculosis Prevention in Healthcare Settings: A Narrative Review. Tropical Medicine and Infectious Disease 2025;10(11):316 View
  4. Adam Essa M. Diagnostic accuracy of AI in chest radiography for pneumonia and lung cancer: A meta-analysis. European Journal of Radiology Open 2025;15:100701 View
  5. Ranjan D, Balkhande D, Tembhare L, Anant More , Mahajan S, Razakova R, Atamuratova Z. Addressing diagnostic resource imbalance in pulmonary tuberculosis detection from chest radiographs through cost-aware learning. Indian Journal of Tuberculosis 2025 View
  6. Kumar J, Bewoor L, Kadam K, Sangve S, Satpute M. Hierarchical taxonomy-aware modeling for granular drug resistance and disease outcome prediction in multimodal tuberculosis cohorts. Indian Journal of Tuberculosis 2025 View
  7. Ouhammou I, Hami H, Erefai O, Hmouni D, Louragli I. Epidemiological Analysis of Tuberculosis Relapses in Morocco: Leveraging Artificial Intelligence for Predictive Modeling. BIO Web of Conferences 2025;200:01015 View
  8. Gade A, Bhoite A, Vasgi B, Dasari A, Babajanov M, Atamuratova Z. Advancing automated tuberculosis detection in chest radiographs through stable policy optimization with clipped objectives. Indian Journal of Tuberculosis 2025 View