ReviewJournal of clinical medicine2022
Diagnostic Accuracy of the Artificial Intelligence Methods in Medical Imaging for Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis.
Review in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
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.
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Who cites it
28 citing papers in PubMed, 3 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Diagnostic accuracy of artificial intelligence for tuberculosis detection from cough sounds: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
- 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
- Revolutionizing diagnosis of pulmonaryFrontiers in microbiology · 2024Pooled it
- Development of an expert-annotated chest X-ray dataset to support AI validation in tuberculosis diagnosis.Insights into imaging · 2026Article
- Artificial intelligence model outperformed experienced clinicians in differentiating the aetiology of pneumonia on chest computed tomography: a retrospective study.Quantitative imaging in medicine and surgery · 2026Article
- PICO-based assessment and categorization of evidence for digital health interventions: an inductive framework development.Frontiers in digital health · 2026Review
- Integrated Artificial Intelligence Framework for Tuberculosis Treatment Abandonment Prediction: A Multi-Paradigm Approach.Journal of clinical medicine · 2025Article
- Revolutionizing emergency care: an overview of the transformative role of artificial intelligence in diagnosis, triage, and patient management.International journal of emergency medicine · 2025Review
- Evaluation of an ultra-portable X-ray system with automated interpretation for tuberculosis active case finding in carceral settings: a diagnostic test accuracy study.BMC infectious diseases · 2025Article
- Whole-lung computed tomography radiomics combined with clinical features for differentiating multidrug-resistant tuberculosis from drug-sensitive tuberculosis: a retrospective multi-center study.Journal of thoracic disease · 2025Article
- Accuracy of Machine Learning in Identifying Drug Resistance in Tuberculosis: A Systematic Review and Meta-Analysis.Health science reports · 2025Review
- Utilization of Explainable Artificial Intelligence (XAI)-Powered Computer-Aided Detection (CAD) System on Chest X-Ray Abnormalities in Health Check-Ups.Yonago acta medica · 2025Article
- Article
- Artificial intelligence for tuberculosis control: a scoping review of applications in public health.Journal of global health · 2025Article
- Integration of AI and ML in Tuberculosis (TB) Management: From Diagnosis to Drug Discovery.Diseases (Basel, Switzerland) · 2025Review
- The Recent Development in the Diagnosis ofSmart medicine · 2025Review
- A systematic review and meta-analysis of artificial intelligence software for tuberculosis diagnosis using chest X-ray imaging.Journal of thoracic disease · 2025Article
- Rhino-Orbital Mucormycosis With Delayed Diagnosis in an Immunocompromised Patient: A Case Report.Cureus · 2025Article
- Evaluation of an ultra-portable X-ray system with automated interpretation for tuberculosis active case finding in carceral settings: a diagnostic test accuracy study.Research square · 2025Article
- A Framework for Two-class Classification of Pulmonary Tuberculosis using Artificial IntelligenceCurrent medical imaging · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tuberculosis (TB) remains one of the leading causes of death among infectious diseases worldwide. Early screening and diagnosis of pulmonary tuberculosis (PTB) is crucial in TB control, and tend to benefit from artificial intelligence. Here, we aimed to evaluate the diagnostic efficacy of a variety of artificial intelligence methods in medical imaging for PTB. We searched MEDLINE and Embase with the OVID platform to identify trials published update to November 2022 that evaluated the effectiveness of artificial-intelligence-based software in medical imaging of patients with PTB. After data extraction, the quality of studies was assessed using quality assessment of diagnostic accuracy studies 2 (QUADAS-2). Pooled sensitivity and specificity were estimated using a bivariate random-effects model. In total, 3987 references were initially identified and 61 studies were finally included, covering a wide range of 124,959 individuals. The pooled sensitivity and the specificity were 91% (95% confidence interval (CI), 89-93%) and 65% (54-75%), respectively, in clinical trials, and 94% (89-96%) and 95% (91-97%), respectively, in model-development studies. These findings have demonstrated that artificial-intelligence-based software could serve as an accurate tool to diagnose PTB in medical imaging. However, standardized reporting guidance regarding AI-specific trials and multicenter clinical trials is urgently needed to truly transform this cutting-edge technology into clinical practice.
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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.