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

Journal: Journal of medical Internet research
PMID: 40053773

Abstract

BACKGROUND: Tuberculosis (TB) remains a significant health concern, contributing to the highest mortality among infectious diseases worldwide. However, none of the various TB diagnostic tools introduced is deemed sufficient on its own for the diagnostic pathway, so various artificial intelligence (AI)-based methods have been developed to address this issue.

Authors

  • Seng Hansun
  • Ahmadreza Argha
  • Ivan Bakhshayeshi
    Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, Australia.
  • Arya Wicaksana
    Informatics Department, Universitas Multimedia Nusantara, Tangerang, Indonesia.
  • Hamid Alinejad-Rokny
    Systems Biology and Health Data Analytics Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, 2052 Sydney, Australia; School of Computer Science and Engineering, The University of New South Wales (UNSW Sydney), 2052 Sydney, Australia; Health Data Analytics Program Leader, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney 2109, Australia.
  • Greg J Fox
    NHMRC Clinical Trials Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
  • Siaw-Teng Liaw
    School of Public Health and Community Medicine, University of New South Wales, Australia. Electronic address: siaw@unsw.edu.au.
  • Branko G Celler
  • Guy B Marks

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