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

Formative Evaluation of Digital Health Interventions (3229) Imaging Informatics (302) Computer-Aided Diagnosis (251) Respiratory Medicine (157) Medical imaging (110)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55641, first published .
Comparing the Output of an Artificial Intelligence Algorithm in Detecting Radiological Signs of Pulmonary Tuberculosis in Digital Chest X-Rays and Their Smartphone-Captured Photos of X-Ray Films: Retrospective Study

Comparing the Output of an Artificial Intelligence Algorithm in Detecting Radiological Signs of Pulmonary Tuberculosis in Digital Chest X-Rays and Their Smartphone-Captured Photos of X-Ray Films: Retrospective Study

Comparing the Output of an Artificial Intelligence Algorithm in Detecting Radiological Signs of Pulmonary Tuberculosis in Digital Chest X-Rays and Their Smartphone-Captured Photos of X-Ray Films: Retrospective Study

Authors of this article:

Smriti Ridhi1 Author Orcid Image ;   Dennis Robert2 Author Orcid Image ;   Pitamber Soren3 Author Orcid Image ;   Manish Kumar3 Author Orcid Image ;   Saniya Pawar4 Author Orcid Image ;   Bhargava Reddy4 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Ghosh M, Lahiri M, Dalal A, Parida K, Kalia N. Advancements in tuberculosis diagnostics: An update. Microbial Pathogenesis 2025;207:107843 View
  2. Aurangzeb B, Robert D, Baard C, Qureshi A, Shaheen A, Ambreen A, McFarlane D, Javed H, Bano I, Chiramal J, Workman L, Pillay T, Franckling-Smith Z, Mustafa T, Andronikou S, Zar H. Evaluating the accuracy of artificial intelligence-powered chest X-ray diagnosis for paediatric pulmonary tuberculosis (EVAL-PAEDTBAID): Study protocol for a multi-centre diagnostic accuracy study. BMJ Open 2025;15(7):e105881 View

Books/Policy Documents

  1. Bueno de Mesquita J. Improving Societal Systems to End Tuberculosis. View