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

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 am...

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Detalles Bibliográficos
Autores principales: Geric, C., Qin, Z. Z., Denkinger, C. M., Kik, S. V., Marais, B., Anjos, A., David, P-M., Khan, F. Ahmad, Trajman, A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union Against Tuberculosis and Lung Disease 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10171486/
https://www.ncbi.nlm.nih.gov/pubmed/37143227
http://dx.doi.org/10.5588/ijtld.22.0687
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author Geric, C.
Qin, Z. Z.
Denkinger, C. M.
Kik, S. V.
Marais, B.
Anjos, A.
David, P-M.
Khan, F. Ahmad
Trajman, A.
author_facet Geric, C.
Qin, Z. Z.
Denkinger, C. M.
Kik, S. V.
Marais, B.
Anjos, A.
David, P-M.
Khan, F. Ahmad
Trajman, A.
author_sort Geric, C.
collection PubMed
description 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.
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spelling pubmed-101714862023-05-11 The rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination Geric, C. Qin, Z. Z. Denkinger, C. M. Kik, S. V. Marais, B. Anjos, A. David, P-M. Khan, F. Ahmad Trajman, A. Int J Tuberc Lung Dis Minireview 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. International Union Against Tuberculosis and Lung Disease 2023-05 2023-05-01 /pmc/articles/PMC10171486/ /pubmed/37143227 http://dx.doi.org/10.5588/ijtld.22.0687 Text en © 2023 The Union https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0 (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
spellingShingle Minireview
Geric, C.
Qin, Z. Z.
Denkinger, C. M.
Kik, S. V.
Marais, B.
Anjos, A.
David, P-M.
Khan, F. Ahmad
Trajman, A.
The rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination
title The rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination
title_full The rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination
title_fullStr The rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination
title_full_unstemmed The rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination
title_short The rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination
title_sort rise of artificial intelligence reading of chest x-rays for enhanced tb diagnosis and elimination
topic Minireview
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10171486/
https://www.ncbi.nlm.nih.gov/pubmed/37143227
http://dx.doi.org/10.5588/ijtld.22.0687
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