ArticleJournal of thoracic disease2025
A systematic review and meta-analysis of artificial intelligence software for tuberculosis diagnosis using chest X-ray imaging.
Article in Journal of thoracic disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07517471 (Start4All - Start Taking Action for TB Diagnosis SCREEN-TB), which is not on this map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Start4All - Start Taking Action for TB Diagnosis SCREEN-TB
Who cites it
10 citing papers in PubMed.
- Implementation of Digital Chest X-Ray with Computer-Aided Detection for Tuberculosis Screening Among Persons with Advanced HIV Disease in Maputo, Mozambique: A Retrospective Cohort Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Can AI substitute the first reader in chest radiograph screening? A retrospective non-inferiority evaluation.Japanese journal of radiology · 2026Article
- Exploring tuberculosis physicians' preferences for AI explainability in China: a protocol for a discrete choice experiment.BMJ open · 2026Article
- Clinical artificial intelligence (AI) in Japan: Regulatory pathways, domain-specific evidence, and its data infrastructure from an international perspective.Global health & medicine · 2026Review
- Development and validation of a machine learning model for community-based tuberculosis screening among persons aged ≥ 15 years in South Africa and Zambia.medRxiv : the preprint server for health sciences · 2026Article
- Challenges and Opportunities for Improved Tuberculosis and HIV Prevention in South America.Open forum infectious diseases · 2026Review
- Tuberculosis Diagnostic Methods: Clinical Applicability, Implementation Challenges, and Integrated Testing Strategies.Pathogens (Basel, Switzerland) · 2026Review
- Diagnostic accuracy of AI-assisted chest radiographs in tuberculosis screening: A Ghanaian clinical study.PloS one · 2026Article
- An ensemble model approach for identifying pulmonary tuberculosis on chest X-ray.Frontiers in medicine · 2026Article
- Diagnostic Performance of Artificial Intelligence in Detecting COVID-19 Pneumonia on Chest Imaging.Cureus · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Pulmonary tuberculosis (PTB) remains a global public health challenge, with 10.8 million new cases reported in 2023. Early diagnosis is crucial for controlling its spread, yet traditional sputum-based tests face limitations in turnaround time and resource availability. Chest X-ray (CXR) is a cost-effective diagnostic tool, but its use in high-tuberculosis (TB) burden regions is restricted by a shortage of radiologists. Artificial intelligence (AI)-based computer-aided detection (CAD) systems, leveraging deep learning, offer a promising solution for automated PTB detection. However, variability in diagnostic performance across AI tools and the need for scenario-specific threshold adjustments remain challenges that need to be addressed. Our meta-analysis evaluated the diagnostic accuracy of five AI-based PTB detection products, aiming to provide insights for advancing AI applications in TB screening and diagnosis. Methods: The PubMed, Embase, Web of Science, and Cochrane Library databases were searched for literature related to CXR diagnosis of TB based on AI technology published from the establishment day of the database to December 19, 2024. The keywords were "artificial intelligence", "tuberculosis", "chest X-ray", and "diagnosis". The literature search, screening, data extraction, quality evaluation, and bias risk assessment were conducted independently by two researchers, and Stata 17.0 software (StataCorp) was used to process and analyze the data. Results: A total of 5,651 references were retrieved, and 21 references were finally selected according to the inclusion and exclusion criteria. The meta-analysis included five software solutions for CXR analysis: JF CXR-1 (JF Healthcare, Nanchang, China), qXR (Qure.ai, Mumbai, India), Lunit INSIGHT CXR (Lunit, Seoul, South Korea), CAD4TB (Delft Imaging, 's-Hertogenbosch, Netherlands), and InferRead DR Chest (Infervision, Beijing, China). Their sensitivity and specificity were as follows: JF CXR-1, 86.0% and 80.0%; qXR, 90.0% and 64.0%; Lunit INSIGHT CXR, 90.0% and 63.0%; CAD4TB, 91.0% and 60.0%; InferRead DR Chest, 89.0% and 59.0%. Conclusions: AI software has demonstrated excellent diagnostic performance in assisting the CXR diagnosis of TB and can help clinicians to make rapid and accurate decisions in screening and treating patients with TB.
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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.