ArticleThe European respiratory journal2024
Accuracy of CAD4TB (Computer-Aided Detection for Tuberculosis) on paediatric chest radiographs.
Article in The European respiratory journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Predicting radiological severity of pulmonary tuberculosis in children: an assessment of the WHO-criteria and novel prediction scores on an individual participant dataset.medRxiv : the preprint server for health sciences · 2026Article
- The role of medical imaging in translational research into early pulmonary tuberculosis.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2026Review
- Bridging the gap: the need for pediatric-specific AI models for tuberculosis detection on chest radiographs.Annals of medicine and surgery (2012) · 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
- Article
- Improving Tuberculosis Diagnosis Through Artificial Intelligence (CAD4TB) and Stool Xpert MTB/RIF Testing: A Prospective Study From Oromia, Ethiopia.Open forum infectious diseases · 2026Article
- Contribution of artificial intelligence to the imaging diagnosis of pediatric pulmonary tuberculosis.Revista do Instituto de Medicina Tropical de Sao Paulo · 2026Article
- From gaps to progress: five-year global advances in the diagnosis of pediatric tuberculosis.Frontiers in pediatrics · 2026Review
- Multi-view deep learning framework for the detection of chest X-rays compatible with pediatric pulmonary tuberculosis.Nature communications · 2025Article
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11 authors.
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Abstract
backgroundComputer-aided detection (CAD) systems hold promise for improving tuberculosis (TB) detection on digital chest radiographs. However, data on their performance in exclusively paediatric populations are scarce.
methodsWe conducted a retrospective diagnostic accuracy study evaluating the performance of CAD4TBv7 (Computer-Aided Detection for Tuberculosis version 7) using digital chest radiographs from well-characterised cohorts of Gambian children aged <15 years with presumed pulmonary TB. The children were consecutively recruited between 2012 and 2022. We measured CAD4TBv7 performance against a microbiological reference standard (MRS) of confirmed TB, and also performed Bayesian latent class analysis (LCA) to address the inherent limitations of the MRS in children. Diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUROC) and point estimates of sensitivity and specificity.
resultsA total of 724 children were included in the analysis, with confirmed TB in 58 (8%), unconfirmed TB in 145 (20%) and unlikely TB in 521 (72%). Using the MRS, CAD4TBv7 showed an AUROC of 0.70 (95% CI 0.60-0.79), and demonstrated sensitivity and specificity of 19.0% (95% CI 11-31%) and 99.0% (95% CI 98.0-100.0%), respectively. Applying Bayesian LCA with the assumption of conditional independence between tests, sensitivity and specificity estimates for CAD4TBv7 were 42.7% (95% CrI 29.2-57.5%) and 97.9% (95% CrI 96.6-98.8%), respectively. When allowing for conditional dependence between culture and Xpert assay, CAD4TBv7 demonstrated a sensitivity of 50.3% (95% CrI 32.9-70.0%) and specificity of 98.0% (95% CrI 96.7-98.9%).
conclusionAlthough CAD4TBv7 demonstrated high specificity, its suboptimal sensitivity underscores the crucial need for optimisation of CAD4TBv7 for detecting TB in children.
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