ArticleQuantitative imaging in medicine and surgery2022
Deep learning-based pulmonary tuberculosis automated detection on chest radiography: large-scale independent testing.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025Pooled it
- Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Weak Localization of Radiographic Manifestations in Pulmonary Tuberculosis from Chest X-ray: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- PulmoX-Net: a channel-attention enhanced deep learning model for multi-class pulmonary pathology classification in chest radiography.Frontiers in medicine · 2026Article
- Using AI system to detect active tuberculosis in a high-prevalence setting on CT scans: a multi-center study.Scientific reports · 2025Article
- Population-scale cross-sectional observational study for AI-powered TB screening on one million CXRs.NPJ digital medicine · 2025Article
- Mobile application prototyping using Artificial Intelligence to support childhood tuberculosis diagnosis.Revista brasileira de enfermagem · 2025Article
- The diagnostic value of hydroxyproline combined with tuberculosis infection T lymphocyte spot assay in pulmonary tuberculosis.Journal of thoracic disease · 2024Article
- Deep Learning-based Diagnosis of Pulmonary Tuberculosis on Chest X-ray in the Emergency Department: A Retrospective Study.Journal of imaging informatics in medicine · 2024Article
- Comparison of deep-learning and radiomics-based machine-learning methods for the identification of chronic obstructive pulmonary disease on low-dose computed tomography images.Quantitative imaging in medicine and surgery · 2024Article
- Differences in pulmonary nodular consolidation and pulmonary cavity among drug-sensitive, rifampicin-resistant and multi-drug resistant tuberculosis patients: the Guangzhou computerized tomography study.Quantitative imaging in medicine and surgery · 2024Article
- Can AI generate diagnostic reports for radiologist approval on CXR images? A multi-reader and multi-case observer performance study.Journal of X-ray science and technology · 2024Article
- Deep Learning-based Diagnosis and Localization of Pneumothorax on Portable Supine Chest X-ray in Intensive and Emergency Medicine: A Retrospective Study.Journal of medical systems · 2023Article
- Deep learning on longitudinal CT scans: automated prediction of treatment outcomes in hospitalized tuberculosis patients.iScience · 2023Article
- Artificial Intelligence in Paediatric Tuberculosis.Pediatric radiology · 2023Review
- 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
- Impact of localized fine tuning in the performance of segmentation and classification of lung nodules from computed tomography scans using deep learning.Frontiers in oncology · 2023Article
- Diagnostic Accuracy of the Artificial Intelligence Methods in Medical Imaging for Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2022Review
Corrections and comments
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Authors and funding
7 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: It is critical to have a deep learning-based system validated on an external dataset before it is used to assist clinical prognoses. The aim of this study was to assess the performance of an artificial intelligence (AI) system to detect tuberculosis (TB) in a large-scale external dataset. Methods: An artificial, deep convolutional neural network (DCNN) was developed to differentiate TB from other common abnormalities of the lung on large-scale chest X-ray radiographs. An internal dataset with 7,025 images was used to develop the AI system, including images were from five sources in the U.S. and China, after which a 6-year dynamic cohort accumulation dataset with 358,169 images was used to conduct an independent external validation of the trained AI system. Results: The developed AI system provided a delineation of the boundaries of the lung region with a Dice coefficient of 0.958. It achieved an AUC of 0.99 and an accuracy of 0.948 on the internal data set, and an AUC of 0.95 and an accuracy of 0.931 on the external data set when it was used to detect TB from normal images. The AI system achieved an AUC of more than 0.9 on the internal data set, and an AUC of over 0.8 on the external data set when it was applied to detect TB, non-TB abnormal and normal images. Conclusions: We conducted a real-world independent validation, which showed that the trained system can be used as a TB screening tool to flag possible cases for rapid radiologic review and guide further examinations for 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.