SynthesisClinical infectious diseases : an official publication of the Infectious Diseases Society of America2022
Chest X-ray Analysis With Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: An Individual Patient Data Meta-Analysis of Diagnostic Accuracy.
Synthesis in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.
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Who cites it
49 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.
- How to diagnose TB in migrants? A systematic review of reviews and decision tree analytical modelling exercise to evaluate properties for single and combined tuberculosis screening tests.The European respiratory journal · 2025Pooled it
- 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
- Article
- Cost-effectiveness of community tuberculosis screening in South Africa.American journal of respiratory and critical care medicine · 2026Article
- Current and Emerging Diagnostic Approaches for Tuberculosis in People Living with HIV: Challenges and Future Perspectives.Tropical medicine and infectious disease · 2026Review
- A mixed-method evaluation of the impact of introducing 52 digital X-ray systems in Ghanaian health facilities on tuberculosis case finding and quality of care.PLOS global public health · 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
- Artificial Intelligence in the Diagnosis and Management of Pulmonary Tuberculosis: A Review of Current Applications and Future Perspectives.Therapeutics and clinical risk management · 2026Review
- Integrating AI with PCR for Tuberculosis Diagnosis: Evaluating a Deep Learning Model for Chest X-Rays.Bioengineering (Basel, Switzerland) · 2025Article
- Global status of policies and practices for systematic TB screening in high-burden countries.IJTLD open · 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
- The performance of computer-aided detection for chest radiography in tuberculosis screening: a population-based retrospective cohort study.Emerging microbes & infections · 2025Article
- Screening and diagnostic strategies for active pulmonary tuberculosis among people in prison in Malaysia: a prospective diagnostic study.The Lancet regional health. Western Pacific · 2025Article
- Accelerating Cough-Based Algorithms for Pulmonary Tuberculosis Screening: Results From the CODA TB DREAM Challenge.Open forum infectious diseases · 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
- Comparing the accuracy of computer-aided detection (CAD) software and radiologists from multiple countries for tuberculosis detection in chest X-Rays.Scientific reports · 2025Article
- Performance of Computer-Aided Detection Software in Tuberculosis Case Finding in Township Health Centers in China.Chronic diseases and translational medicine · 2025Article
- A systematic review and meta-analysis of artificial intelligence software for tuberculosis diagnosis using chest X-ray imaging.Journal of thoracic disease · 2025Article
- Accuracy of CAD4TB (Computer-Aided Detection for Tuberculosis) on paediatric chest radiographs.The European respiratory journal · 2024Article
Corrections and comments
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Authors and funding
19 authors at 11 institutions in 8 countries.
Funding
Abstract
backgroundAutomated radiologic analysis using computer-aided detection software (CAD) could facilitate chest X-ray (CXR) use in tuberculosis diagnosis. There is little to no evidence on the accuracy of commercially available deep learning-based CAD in different populations, including patients with smear-negative tuberculosis and people living with human immunodeficiency virus (HIV, PLWH).
methodsWe collected CXRs and individual patient data (IPD) from studies evaluating CAD in patients self-referring for tuberculosis symptoms with culture or nucleic acid amplification testing as the reference. We reanalyzed CXRs with three CAD programs (CAD4TB version (v) 6, Lunit v3.1.0.0, and qXR v2). We estimated sensitivity and specificity within each study and pooled using IPD meta-analysis. We used multivariable meta-regression to identify characteristics modifying accuracy.
resultsWe included CXRs and IPD of 3727/3967 participants from 4/7 eligible studies. 17% (621/3727) were PLWH. 17% (645/3727) had microbiologically confirmed tuberculosis. Despite using the same threshold score for classifying CXR in every study, sensitivity and specificity varied from study to study. The software had similar unadjusted accuracy (at 90% pooled sensitivity, pooled specificities were: CAD4TBv6, 56.9% [95% confidence interval {CI}: 51.7-61.9]; Lunit, 54.1% [95% CI: 44.6-63.3]; qXRv2, 60.5% [95% CI: 51.7-68.6]). Adjusted absolute differences in pooled sensitivity between PLWH and HIV-uninfected participants were: CAD4TBv6, -13.4% [-21.1, -6.9]; Lunit, +2.2% [-3.6, +6.3]; qXRv2: -13.4% [-21.5, -6.6]; between smear-negative and smear-positive tuberculosis was: were CAD4TBv6, -12.3% [-19.5, -6.1]; Lunit, -17.2% [-24.6, -10.5]; qXRv2, -16.6% [-24.4, -9.9]. Accuracy was similar to human readers.
conclusionsFor CAD CXR analysis to be implemented as a high-sensitivity tuberculosis rule-out test, users will need threshold scores identified from their own patient populations and stratified by HIV and smear status.
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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.