ArticleEuropean radiology2021
Deep learning-based automated detection algorithm for active pulmonary tuberculosis on chest radiographs: diagnostic performance in systematic screening of asymptomatic individuals.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.Journal of medical Internet research · 2025Pooled it
- A Systematic Review of Deep Learning Techniques for Tuberculosis Detection From Chest Radiograph.Frontiers in medicine · 2022Pooled it
- Effect of Big Data Analysis-Based Remote Management Combined with Yangyin Runfei Decoction on Coagulation Function, Pulmonary Function, and Quality of Life of Pulmonary Tuberculosis Patients.Computational intelligence and neuroscience · 2022Trial
- Using AI system to detect active tuberculosis in a high-prevalence setting on CT scans: a multi-center study.Scientific reports · 2025Article
- Application of Artificial Intelligence in Thoracic Radiology: A Narrative Review.Tuberculosis and respiratory diseases · 2025Article
- A Framework for Two-class Classification of Pulmonary Tuberculosis using Artificial IntelligenceCurrent medical imaging · 2025Article
- AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography.Journal of personalized medicine · 2024Article
- AI for Detection of Tuberculosis: Implications for Global Health.Radiology. Artificial intelligence · 2024Article
- The predictors of voluntary participation in pulmonary tuberculosis screening program: a study in a suburban community of southern Thailand.Frontiers in public health · 2024Article
- Effect of multimodal diagnostic approach using deep learning-based automated detection algorithm for active pulmonary tuberculosis.Scientific reports · 2023Observational
- Emerging infectious disease surveillance using a hierarchical diagnosis model and the Knox algorithm.Scientific reports · 2023Article
- Pulmonary abnormality screening on chest x-rays from different machine specifications: a generalized AI-based image manipulation pipeline.European radiology experimental · 2023Article
- Article
- AI-clinician collaboration via disagreement prediction: A decision pipeline and retrospective analysis of real-world radiologist-AI interactions.Cell reports. Medicine · 2023Article
- The Performance of a Deep Learning-Based Automatic Measurement Model for Measuring the Cardiothoracic Ratio on Chest Radiographs.Bioengineering (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Paediatric Tuberculosis.Pediatric radiology · 2023Review
- Multicentre external validation of a commercial artificial intelligence software to analyse chest radiographs in health screening environments with low disease prevalence.European radiology · 2023Article
- Clinical assistant decision-making model of tuberculosis based on electronic health records.BioData mining · 2023Article
- Development and validation of a 3D-convolutional neural network model based on chest CT for differentiating active pulmonary tuberculosis from community-acquired pneumonia.La Radiologia medica · 2023Article
- A prospective multicenter clinical research study validating the effectiveness and safety of a chest X-ray-based pulmonary tuberculosis screening software JF CXR-1 built on a convolutional neural network algorithm.Frontiers in medicine · 2023Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesPerformance of deep learning-based automated detection (DLAD) algorithms in systematic screening for active pulmonary tuberculosis is unknown. We aimed to validate DLAD algorithm for detection of active pulmonary tuberculosis and any radiologically identifiable relevant abnormality on chest radiographs (CRs) in this setting.
methodsWe performed out-of-sample testing of a pre-trained DLAD algorithm, using CRs from 19.686 asymptomatic individuals (ages, 21.3 ± 1.9 years) as part of systematic screening for tuberculosis between January 2013 and July 2018. Area under the receiver operating characteristic curves (AUC) for diagnosis of tuberculosis and any relevant abnormalities were measured. Accuracy measures including sensitivities, specificities, positive predictive values (PPVs), and negative predictive values (NPVs) were calculated at pre-defined operating thresholds (high sensitivity threshold, 0.16; high specificity threshold, 0.46).
resultsAll five CRs from four individuals with active pulmonary tuberculosis were correctly classified as having abnormal findings by DLAD with specificities of 0.959 and 0.997, PPVs of 0.006 and 0.068, and NPVs of both 1.000 at high sensitivity and high specificity thresholds, respectively. With high specificity thresholds, DLAD showed comparable diagnostic measures with the pooled radiologists (p values > 0.05). For the radiologically identifiable relevant abnormality (n = 28), DLAD showed an AUC value of 0.967 (95% confidence interval, 0.938-0.996) with sensitivities of 0.821 and 0.679, specificities of 0.960 and 0.997, PPVs of 0.028 and 0.257, and NPVs of both 0.999 at high sensitivity and high specificity thresholds, respectively.
conclusionsIn systematic screening for tuberculosis in a low-prevalence setting, DLAD algorithm demonstrated excellent diagnostic performance, comparable with the radiologists in the detection of active pulmonary tuberculosis. KEY POINTS: • Deep learning-based automated detection algorithm detected all chest radiographs with active pulmonary tuberculosis with high specificities and negative predictive values in systematic screening. • Deep learning-based automated detection algorithm had comparable diagnostic measures with the radiologists for detection of active pulmonary tuberculosis on chest radiographs. • For the detection of radiologically identifiable relevant abnormalities on chest radiographs, deep learning-based automated detection algorithm showed excellent diagnostic performance in systematic screening.
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