ArticleScientific reports2021
Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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The trial behind it
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Who cites it
63 citing papers in PubMed, 95 citations in OpenAlex.
- Evaluation of C-Reactive Protein and Computer-Aided Analysis of Chest X-rays as Tuberculosis Triage Tests at Health Facilities in Lesotho and South Africa.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024Trial
- Tuberculosis Disease Severity Assessment Using Clinical Variables and Radiology Enabled by Artificial Intelligence.The Journal of infectious diseases · 2026Article
- Development of an expert-annotated chest X-ray dataset to support AI validation in tuberculosis diagnosis.Insights into imaging · 2026Article
- A public-private partnership for facility-based tuberculosis screening using chest radiographs, Viet Nam.Bulletin of the World Health Organization · 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
- Accelerating tuberculosis diagnosis in Mozambican prisons using digital chest X-rays with computer-aided detection: a longitudinal, comprehensive health intervention.BMC global and public health · 2026Article
- Computer-Aided Analysis of Photographed Chest X-Ray Films Performs Well Compared With Trained Radiologists.Mayo Clinic proceedings. Digital health · 2026Article
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- 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
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- Implementing computer‑aided detection for TB active case finding: A qualitative study of feasibility and acceptability in Tondo, Manila.PLOS global public health · 2026Article
- The Case for Local AI Development: Lessons From Computer‑Aided Detection of Tuberculosis and Silicosis in Southern Africa's Ex‑Miners.Annals of global health · 2026Article
- Computer-aided detection for radiological disease severity classification on chest radiograph in children with intra-thoracic tuberculosis.PLOS global public health · 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
- AI-Powered Chest X-Ray for Diagnosing Pulmonary Tuberculosis in County and Township Health Care Facilities in Yichang: Retrospective, Real-World Study.Journal of medical Internet research · 2025Article
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- 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
- Performance of CAD4TB artificial intelligence technology in TB screening programmes among the adult population in South Africa and Lesotho.Journal of clinical tuberculosis and other mycobacterial diseases · 2025Article
- Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There have been few independent evaluations of computer-aided detection (CAD) software for tuberculosis (TB) screening, despite the rapidly expanding array of available CAD solutions. We developed a test library of chest X-ray (CXR) images which was blindly re-read by two TB clinicians with different levels of experience and then processed by 12 CAD software solutions. Using Xpert MTB/RIF results as the reference standard, we compared the performance characteristics of each CAD software against both an Expert and Intermediate Reader, using cut-off thresholds which were selected to match the sensitivity of each human reader. Six CAD systems performed on par with the Expert Reader (Qure.ai, DeepTek, Delft Imaging, JF Healthcare, OXIPIT, and Lunit) and one additional software (Infervision) performed on par with the Intermediate Reader only. Qure.ai, Delft Imaging and Lunit were the only software to perform significantly better than the Intermediate Reader. The majority of these CAD software showed significantly lower performance among participants with a past history of TB. The radiography equipment used to capture the CXR image was also shown to affect performance for some CAD software. TB program implementers now have a wide selection of quality CAD software solutions to utilize in their CXR screening initiatives.
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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.