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

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dc.contributor.authorGeric, C.
dc.contributor.authorQin, Z. Z.
dc.contributor.authorDenkinger, C. M.
dc.contributor.authorKik, S. V.
dc.contributor.authorMarais, B.
dc.contributor.authorAnjos, A.
dc.contributor.authorDavid, P-M.
dc.contributor.authorKhan, F. Ahmad
dc.contributor.authorTrajman, A.
dc.date.accessioned2024-12-12T18:31:50Z
dc.date.available2024-12-12T18:31:50Z
dc.date.issued2023
dc.description.abstractWe provide an overview of the latest evidence on computer-aided detection (CAD) software for auto- mated 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.
dc.identifier.citationGeric C, Qin ZZ, Denkinger CM, Kik SV, Marais B, Anjos A, David PM, Ahmad Khan F, Trajman A. The rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination. Int J Tuberc Lung Dis. 2023 May 1;27(5):367-372. doi: 10.5588/ijtld.22.0687.
dc.identifier.otherDOI: 10.5588/ijtld.22.0687
dc.identifier.urihttps://dspace.inc.saude.gov.br/handle/123456789/710
dc.language.isoen
dc.publisherInternational Journal of Tuberculosis and Lung Disease
dc.subjectcomputer-aided detectionen
dc.subjectchest radiologyen
dc.subjectpulmonary diseaseen
dc.subjecttuberculosisen
dc.subjectAI technology.en
dc.titleThe rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination
dc.typeArticle
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