ArticlePLOS global public health2023
Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children.
Article in PLOS global public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of the WHO tuberculosis treatment decision algorithms for children with presumptive tuberculosis: An individual participant data meta-analysis.PLoS medicine · 2025Pooled it
- 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
- 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
- From gaps to progress: five-year global advances in the diagnosis of pediatric tuberculosis.Frontiers in pediatrics · 2026Review
- Contribution of artificial intelligence to the imaging diagnosis of pediatric pulmonary tuberculosis.Revista do Instituto de Medicina Tropical de Sao Paulo · 2026Article
- Long-segment tracheal stenosis with left pulmonary artery sling in a low- to middle-income country setting: Value of imaging and bronchoscopy: CASE REPORT.African journal of thoracic and critical care medicine · 2026Article
- Multi-view deep learning framework for the detection of chest X-rays compatible with pediatric pulmonary tuberculosis.Nature communications · 2025Article
- Article
- Artificial Intelligence in Thoracic Surgery: Transforming Diagnostics, Treatment, and Patient Outcomes.Diagnostics (Basel, Switzerland) · 2025Review
- Breaks in the Cascade of Care: Evidence to Shift From Facility-Based Screening of Household Contacts of People With Tuberculosis in Rural Tanzania.Journal of the Pediatric Infectious Diseases Society · 2025Article
- Article
- Closing the gaps in childhood TB detection.IJTLD open · 2025Article
- Impact of the use of the ultra-portable digital x-ray with CAD4TB for active case finding for tuberculosis in Nigeria.Frontiers in digital health · 2025Article
- Diagnostic performance of chest radiography for pediatric tuberculosis across high- and low-burden settings.Frontiers in pediatrics · 2025Article
- Applications and prospects of artificial intelligence in the auxiliary diagnosis of pediatric pulmonary tuberculosis.Frontiers in microbiology · 2025Review
- Assessment of radiological capacity and disparities in TB diagnosis: a comparative study of Mozambique, South Africa and Spain.BMJ public health · 2024Article
- Accuracy of CAD4TB (Computer-Aided Detection for Tuberculosis) on paediatric chest radiographs.The European respiratory journal · 2024Article
- External quality assurance of chest X-ray interpretation to strengthen diagnosis of childhood TB.IJTLD open · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Diagnostic tools for paediatric tuberculosis remain limited, with heavy reliance on clinical algorithms which include chest x-ray. Computer aided detection (CAD) for tuberculosis on chest x-ray has shown promise in adults. We aimed to measure and optimise the performance of an adult CAD system, CAD4TB, to identify tuberculosis on chest x-rays from children with presumptive tuberculosis. Chest x-rays from 620 children <13 years enrolled in a prospective observational diagnostic study in South Africa, were evaluated. All chest x-rays were read by a panel of expert readers who attributed each with a radiological reference of either 'tuberculosis' or 'not tuberculosis'. Of the 525 chest x-rays included in this analysis, 80 (40 with a reference of 'tuberculosis' and 40 with 'not tuberculosis') were allocated to an independent test set. The remainder made up the training set. The performance of CAD4TB to identify 'tuberculosis' versus 'not tuberculosis' on chest x-ray against the radiological reference read was calculated. The CAD4TB software was then fine-tuned using the paediatric training set. We compared the performance of the fine-tuned model to the original model. Our findings were that the area under the receiver operating characteristic curve (AUC) of the original CAD4TB model, prior to fine-tuning, was 0.58. After fine-tuning there was an improvement in the AUC to 0.72 (p = 0.0016). In this first-ever description of the use of CAD to identify tuberculosis on chest x-ray in children, we demonstrate a significant improvement in the performance of CAD4TB after fine-tuning with a set of well-characterised paediatric chest x-rays. CAD has the potential to be a useful additional diagnostic tool for paediatric tuberculosis. We recommend replicating the methods we describe using a larger chest x-ray dataset from a more diverse population and evaluating the potential role of CAD to replace a human-read chest x-ray within treatment-decision algorithms for paediatric tuberculosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.