ArticleNPJ digital medicine2021
Computer-aided interpretation of chest radiography reveals the spectrum of tuberculosis in rural South Africa.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 48 papers, 1 of them a synthesis that pooled it.
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Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- A Systematic Review of Deep Learning Techniques for Tuberculosis Detection From Chest Radiograph.Frontiers in medicine · 2022Pooled it
- Towards ending tuberculosis in South Africa: an uncertainty analysis of the programmes and factors most critical to future declines.Global health action · 2026Article
- The impact and cost of using mobile digital X-ray units to detect pulmonary TB in South Africa.IJTLD open · 2026Article
- Cost-effectiveness of community tuberculosis screening in South Africa.American journal of respiratory and critical care medicine · 2026Article
- Artificial intelligence on chest X-ray for tuberculosis screening in Tanzania: a multicentre evaluation.BMC infectious diseases · 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
- Beyond accuracy: evaluating the operational feasibility and diagnostic yield of CAD4TB vs. Timika score for scalable TB screening in low-resource settings.Frontiers in digital health · 2026Article
- Operationalising Inclusion for Participatory Design: Worked Examples for TRIPOD+AI & PROBAST+AI.Health services insights · 2026Article
- Article
- Cost-effectiveness analysis of computer aided detection for tuberculosis in Ghana: A decision-analytic modeling study.PloS one · 2026Article
- Performance of universal and stratified computer-aided detection thresholds for chest x-ray-based tuberculosis screening: a cross-sectional, diagnostic accuracy study.The Lancet. Digital health · 2025Article
- Performance of chest X-ray with computer-aided detection powered by deep learning-based artificial intelligence for tuberculosis presumptive identification during case finding in the Philippines.BMC global and public health · 2025Article
- Article
- Detection of pulmonary tuberculosis through mobile X-ray based active case-finding in Pakistan: a retrospective analysis from programmatic screening of1 214 289 individuals from 2017 to 2021.BMJ global health · 2025Article
- Implementation costs and cost-effectiveness of ultraportable chest X-ray with artificial intelligence in active case finding for tuberculosis in Nigeria.PLOS digital health · 2025Article
- Comparative analysis of the impact of portable digital X-ray on TB screening in hard-to-reach areas in Nigeria.Public health action · 2025Article
- Evidence ofFrontiers in veterinary science · 2025Article
- Accuracy of CAD4TB (Computer-Aided Detection for Tuberculosis) on paediatric chest radiographs.The European respiratory journal · 2024Article
- Computer-aided detection of tuberculosis from chest radiographs in a tuberculosis prevalence survey in South Africa: external validation and modelled impacts of commercially available artificial intelligence software.The Lancet. Digital health · 2024Article
- Prior tuberculosis, radiographic lung abnormalities and prevalent diabetes in rural South Africa.BMC infectious diseases · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
19 authors at 6 institutions in 4 countries.
Funding
Abstract
Computer-aided digital chest radiograph interpretation (CAD) can facilitate high-throughput screening for tuberculosis (TB), but its use in population-based active case-finding programs has been limited. In an HIV-endemic area in rural South Africa, we used a CAD algorithm (CAD4TBv5) to interpret digital chest x-rays (CXR) as part of a mobile health screening effort. Participants with TB symptoms or CAD4TBv5 score above the triaging threshold were referred for microbiological sputum assessment. During an initial pilot phase, a low CAD4TBv5 triaging threshold of 25 was selected to maximize TB case finding. We report the performance of CAD4TBv5 in screening 9,914 participants, 99 (1.0%) of whom were found to have microbiologically proven TB. CAD4TBv5 was able to identify TB cases at the same sensitivity but lower specificity as a blinded radiologist, whereas the next generation of the algorithm (CAD4TBv6) achieved comparable sensitivity and specificity to the radiologist. The CXRs of people with microbiologically confirmed TB spanned a range of lung field abnormality, including 19 (19.2%) cases deemed normal by the radiologist. HIV serostatus did not impact CAD4TB's performance. Notably, 78.8% of the TB cases identified during this population-based survey were asymptomatic and therefore triaged for sputum collection on the basis of CAD4TBv5 score alone. While CAD4TBv6 has the potential to replace radiologists for triaging CXRs in TB prevalence surveys, population-specific piloting is necessary to set the appropriate triaging thresholds. Further work on image analysis strategies is needed to identify radiologically subtle active TB.
Identifiers
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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.