ArticlePloS one2026
Diagnostic accuracy of AI-assisted chest radiographs in tuberculosis screening: A Ghanaian clinical study.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Artificial Intelligence-Assisted Quantification of Longitudinal HRCT Changes During Treatment of Pulmonary Tuberculosis: An Exploratory Proof-of-Concept Study.Diagnostics (Basel, Switzerland) · 2026Article
- Cost-effectiveness analysis of computer aided detection for tuberculosis in Ghana: A decision-analytic modeling study.PloS one · 2026Article
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Authors and funding
7 authors.
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Abstract
backgroundTuberculosis remains a major global health challenge, particularly in resource-limited settings where access to expert radiological interpretation is constrained. Artificial intelligence offers a promising solution to enhance diagnostic accuracy and efficiency in TB screening.
aimThis study aimed to evaluate the diagnostic performance of an AI-based system compared to a radiologist in screening for TB using chest X-rays from 1,010 patients.
methodsPatients were adults ≥18 years with suspected TB in a high-burden setting. GeneXpert MTB/RIF served as reference to assess accuracy, sensitivity, specificity, PPV, NPV, and AUC for radiologist and AI TB predictions. Comparisons used McNemar's test and Cohen's kappa to evaluate agreement and significance of differences.
resultsThe AI system demonstrated superior performance with an accuracy of 91%, sensitivity of 86%, specificity of 93%, PPV of 85%, NPV of 94%, and AUC of 0.90. In contrast, the radiologist achieved an accuracy of 86%, sensitivity of 84%, specificity of 87%, PPV of 76%, NPV of 92%, and AUC of 0.86. McNemar's test revealed a statistically significant difference between the two modalities (p = 0.0021). Cohen's kappa indicated substantial agreement between AI and GeneXpert MTB/RIF result (κ = 0.79), moderate agreement for the radiologist and GeneXpert MTB/RIF result (κ = 0.69), and moderate agreement between radiologist and AI predictions (κ = 0.53).
conclusionThe AI system outperformed the radiologist in TB screening, demonstrating higher diagnostic accuracy and agreement with GeneXpert MTB/RIF result. These findings support the integration of AI into TB screening workflows, particularly in settings with limited access to expert radiological interpretation.
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