ArticleScientific reports2025
Comparing the accuracy of computer-aided detection (CAD) software and radiologists from multiple countries for tuberculosis detection in chest X-Rays.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- User perspectives on the Molbio Truenat platform and tuberculosis assays for decentralised testing in Mozambique and Tanzania.BMJ global health · 2026Trial
- Implementation of Digital Chest X-Ray with Computer-Aided Detection for Tuberculosis Screening Among Persons with Advanced HIV Disease in Maputo, Mozambique: A Retrospective Cohort Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Performance of Human and Computer-aided Evaluation of Digital Chest Radiography for Community-based Screening of Asymptomatic Tuberculosis.medRxiv : the preprint server for health sciences · 2026Article
- Bridging the analog divide: a comparison of printed X-ray films and digital images when using computer-aided detection software for tuberculosis screening.BMC global and public health · 2026Article
- Computer-aided detection for radiological disease severity classification on chest radiograph in children with intra-thoracic tuberculosis.PLOS global public health · 2026Article
- Comparison of different Lunit INSIGHT CXR software versions when reading chest radiographs for tuberculosis.PLOS digital health · 2025Article
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Authors and funding
16 authors.
Funding
Abstract
Nearly a third of TB cases go undetected annually. WHO recommends computer-aided detection (CAD) to enhance TB screening, with studies showing comparable performance to local radiologists. Using 774 chest X-rays from the South African National TB Prevalence Survey, we compared 12 CAD software with 11 radiologists from Nigeria, India, the UK, and the US, against a composite microbiological reference standard. Sensitivity, specificity and Cohen's kappa were calculated. Receiver-operating characteristic curves were developed for CAD and Euclidean distance assessed radiologists' alignment with the best-performing software. Binomial regression tested the impact of radiologists' characteristics on accuracy. Radiologist performance varied. On the restricted read, British radiologists had the highest sensitivity (78.7% [73.2-83.5%]) and Indian radiologists the lowest (67.1% [61.0-72.8%]). Specificity ranged from 75.8% (71.8-79.4%, Nigeria) to 84.3% (80.9-87.3%, the US). Radiologist performance was significantly impacted by HIV, prior TB, and age. The top CAD outperformed all except Indian radiologists when matching specificity. CAD with Conformité Européenne generally matched or surpassed radiologists. British radiologists' sensitivity was closest to the top CAD, while American radiologists were closest in specificity and overall. Experience, TB reads, and country had no significant impact on accuracy. CAD performed well against radiologists globally, highlighting potential to enhance access to care.
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