ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2019
Development and Validation of a Deep Learning-based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs.
Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers, 6 of them syntheses that pooled it.
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113 citing papers in PubMed, 6 syntheses or guidelines pooled it, 229 citations in OpenAlex.
- Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review.Journal of medical Internet research · 2023Pooled it
- Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review.Journal of medical systems · 2022Pooled it
- A Systematic Review of Deep Learning Techniques for Tuberculosis Detection From Chest Radiograph.Frontiers in medicine · 2022Pooled it
- Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application.Current diabetes reports · 2019Pooled it
- Pooled it
- Conventional Versus Artificial Intelligence-Assisted Interpretation of Chest Radiographs in Patients With Acute Respiratory Symptoms in Emergency Department: A Pragmatic Randomized Clinical Trial.Korean journal of radiology · 2023Trial
- Comparison of Chest Radiograph Captions Based on Natural Language Processing vs Completed by Radiologists.JAMA network open · 2023Trial
- Artificial intelligence research in journals indexed in the web of science "infectious diseases" category: a bibliometric analysis, 2016-2025.Infection · 2026Article
- Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.npj health systems · 2026Review
- Multimodal Learning with Privileged Report Supervision for Generalizable Tuberculosis Detection on Chest Radiographs.Journal of medical systems · 2026Article
- An optimized EfficientNetB0 framework with CLAHE-based preprocessing for accurate multi-class chest X-ray classification.Scientific reports · 2026Article
- Decision-Aware Vision Mamba with Context-Guided Slot Mixing for Chest X-Ray Screening and Culture-Based Hierarchical Tuberculosis Classification.Sensors (Basel, Switzerland) · 2026Article
- Artificial intelligence (AI)-driven ensemble model for comprehensive chest X-ray abnormality detection and deployment.The Indian journal of medical research · 2026Article
- An Interpretable Chest X-ray Classification Framework Using Prototype Memory and Counterfactual Consistency.Cureus · 2026Article
- Use of Artificial Intelligence in Diagnosing Vertical Root Fractures-A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Precise discrimination of mycobacterial pulmonary diseases via multimodal machine learning integrating chest CT and clinical markers.Frontiers in medicine · 2026Article
- Article
- Factors associated with false-negative results from the rapid molecular test for pulmonary tuberculosis in the state of São Paulo, Brazil.Revista brasileira de epidemiologia = Brazilian journal of epidemiology · 2026Article
- Integrating AI with PCR for Tuberculosis Diagnosis: Evaluating a Deep Learning Model for Chest X-Rays.Bioengineering (Basel, Switzerland) · 2025Article
53 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDetection of active pulmonary tuberculosis on chest radiographs (CRs) is critical for the diagnosis and screening of tuberculosis. An automated system may help streamline the tuberculosis screening process and improve diagnostic performance.
methodsWe developed a deep learning-based automatic detection (DLAD) algorithm using 54c221 normal CRs and 6768 CRs with active pulmonary tuberculosis that were labeled and annotated by 13 board-certified radiologists. The performance of DLAD was validated using 6 external multicenter, multinational datasets. To compare the performances of DLAD with physicians, an observer performance test was conducted by 15 physicians including nonradiology physicians, board-certified radiologists, and thoracic radiologists. Image-wise classification and lesion-wise localization performances were measured using area under the receiver operating characteristic (ROC) curves and area under the alternative free-response ROC curves, respectively. Sensitivities and specificities of DLAD were calculated using 2 cutoffs (high sensitivity [98%] and high specificity [98%]) obtained through in-house validation.
resultsDLAD demonstrated classification performance of 0.977-1.000 and localization performance of 0.973-1.000. Sensitivities and specificities for classification were 94.3%-100% and 91.1%-100% using the high-sensitivity cutoff and 84.1%-99.0% and 99.1%-100% using the high-specificity cutoff. DLAD showed significantly higher performance in both classification (0.993 vs 0.746-0.971) and localization (0.993 vs 0.664-0.925) compared to all groups of physicians.
conclusionsOur DLAD demonstrated excellent and consistent performance in the detection of active pulmonary tuberculosis on CR, outperforming physicians, including thoracic radiologists.
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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.