ArticlePLOS digital health2022
Comparing different versions of computer-aided detection products when reading chest X-rays for tuberculosis.
Article in PLOS digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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The trial behind it
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Who cites it
31 citing papers in PubMed.
- Global Research Trends and Insights on AI Usage in Tuberculosis: Bibliometric Analysis.JMIR medical informatics · 2026Article
- Exploring tuberculosis physicians' preferences for AI explainability in China: a protocol for a discrete choice experiment.BMJ open · 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
- Diagnostic Accuracy of Tuberculosis Screening Tests in a Prospective Multinational Cohort: Chest Radiography With Computer-Aided Detection, Xpert Tuberculosis Host Response, and C-Reactive Protein.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026Article
- Routine active case finding in Nigerian penitentiary institutions: comparing yields from digital X-ray with CAD4TB and WHO 4-symptom screening.BMC infectious diseases · 2026Article
- Computer-aided detection pragmatic threshold calibration in TB active case finding: A mixed-methods study in Tondo, Manila.PLOS global public health · 2026Article
- 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
- Comparing the accuracy of computer-aided detection (CAD) software and radiologists from multiple countries for tuberculosis detection in chest X-Rays.Scientific reports · 2025Article
- Integration of AI and ML in Tuberculosis (TB) Management: From Diagnosis to Drug Discovery.Diseases (Basel, Switzerland) · 2025Review
- A systematic review and meta-analysis of artificial intelligence software for tuberculosis diagnosis using chest X-ray imaging.Journal of thoracic disease · 2025Article
- Comparison of different Lunit INSIGHT CXR software versions when reading chest radiographs for tuberculosis.PLOS digital health · 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
- Assessment of scattered and leakage radiation from ultra-portable X-ray systems in chest imaging: An independent study.PLOS global public health · 2025Article
- Artificial intelligence: a useful tool in active tuberculosis screening among vulnerable groups in Romania - advantages and limitations.Frontiers in public health · 2025Article
- Lung and abdominal ultrasound accuracy for tuberculosis: An Indian prospective cohort study.PloS one · 2025Article
- Diagnostic Accuracy of Lung and Abdominal Ultrasound for Tuberculosis in a German Multicenter Cohort of Patients With Presumed Tuberculosis Disease.Open forum infectious diseases · 2024Article
- Prospective Multi-Site Validation of AI to Detect Tuberculosis and Chest X-Ray Abnormalities.NEJM AI · 2024Article
- Tuberculosis Diagnosis: Current, Ongoing, and Future Approaches.Diseases (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-aided detection (CAD) was recently recommended by the WHO for TB screening and triage based on several evaluations, but unlike traditional diagnostic tests, software versions are updated frequently and require constant evaluation. Since then, newer versions of two of the evaluated products have already been released. We used a case control sample of 12,890 chest X-rays to compare performance and model the programmatic effect of upgrading to newer versions of CAD4TB and qXR. We compared the area under the receiver operating characteristic curve (AUC), overall, and with data stratified by age, TB history, gender, and patient source. All versions were compared against radiologist readings and WHO's Target Product Profile (TPP) for a TB triage test. Both newer versions significantly outperformed their predecessors in terms of AUC: CAD4TB version 6 (0.823 [0.816-0.830]), version 7 (0.903 [0.897-0.908]) and qXR version 2 (0.872 [0.866-0.878]), version 3 (0.906 [0.901-0.911]). Newer versions met WHO TPP values, older versions did not. All products equalled or surpassed the human radiologist performance with improvements in triage ability in newer versions. Humans and CAD performed worse in older age groups and among those with TB history. New versions of CAD outperform their predecessors. Prior to implementation CAD should be evaluated using local data because underlying neural networks can differ significantly. An independent rapid evaluation centre is necessitated to provide implementers with performance data on new versions of CAD products as they are developed.
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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.