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Home / The International Journal of Tuberculosis and Lung Disease, Volume 27, Number 5

Open Access The rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination

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We provide an overview of the latest evidence on computer-aided detection (CAD) software for automated interpretation of chest radiographs (CXRs) for TB detection. CAD is a useful tool that can assist in rapid and consistent CXR interpretation for TB. CAD can achieve high sensitivity TB detection among people seeking care with symptoms of TB and in population-based screening, has accuracy on-par with human readers. However, implementation challenges remain. Due to diagnostic heterogeneity between settings and sub-populations, users need to select threshold scores rather than use pre-specified ones, but some sites may lack the resources and data to do so. Efficient standardisation is further complicated by frequent updates and new CAD versions, which also challenges implementation and comparison. CAD has not been validated for TB diagnosis in children and its accuracy for identifying non-TB abnormalities remains to be evaluated. A number of economic and political issues also remain to be addressed through regulation for CAD to avoid furthering health inequities. Although CAD-based CXR analysis has proven remarkably accurate for TB detection in adults, the above issues need to be addressed to ensure that the technology meets the needs of high-burden settings and vulnerable sub-populations.

Keywords: AI technology; chest radiology; computer-aided detection; pulmonary disease; tuberculosis

Document Type: Review Article

Affiliations: 1: Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, QC, Canada, Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Montreal, QC, Canada, McGill International TB Centre, Research Institute of the McGill University Health Centre, Montreal, QC, Canada 2: Stop TB Partnership, Geneva, Switzerland, Division of Infectious Diseases and Tropical Medicine, Heidelberg University Hospital, Heidelberg, Germany 3: Division of Infectious Diseases and Tropical Medicine, Heidelberg University Hospital, Heidelberg, Germany, German Centre for Infection Research (DZIF), partner site of Heidelberg University Hospital, Heidelberg, Germany 4: FIND, the Global Alliance for Diagnostics, Geneva, Switzerland 5: Sydney Medical School and Sydney Infectious Diseases Institute, The University of Sydney, Westmead, NSW, Australia 6: Idiap Research Institute, Martigny, Switzerland 7: Faculty of Pharmacy, Université de Montréal, Montréal, QC, Canada 8: Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, QC, Canada, Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Montreal, QC, Canada, McGill International TB Centre, Research Institute of the McGill University Health Centre, Montreal, QC, Canada 9: McGill International TB Centre, Research Institute of the McGill University Health Centre, Montreal, QC, Canada, Departamento de Clínica Médica, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil

Publication date: May 1, 2023

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  • The International Journal of Tuberculosis and Lung Disease (IJTLD) is for clinical research and epidemiological studies on lung health, including articles on TB, TB-HIV and respiratory diseases such as COVID-19, asthma, COPD, child lung health and the hazards of tobacco and air pollution. Individuals and institutes can subscribe to the IJTLD online or in print – simply email us at [email protected] for details.

    The IJTLD is dedicated to understanding lung disease and to the dissemination of knowledge leading to better lung health. To allow us to share scientific research as rapidly as possible, the IJTLD is fast-tracking the publication of certain articles as preprints prior to their publication. Read fast-track articles.

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