ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2021
Identifying Drug-Resistant Tuberculosis in Chest Radiographs: Evaluation of CNN Architectures and Training Strategies.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Automated Pulmonary Tuberculosis Severity Assessment on Chest X-rays.Journal of imaging informatics in medicine · 2024Article
- AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography.Journal of personalized medicine · 2024Article
- Deep learning for precise diagnosis and subtype triage of drug-resistant tuberculosis on chest computed tomography.MedComm · 2024Article
- Advancing tuberculosis management: the role of predictive, preventive, and personalized medicine.Frontiers in microbiology · 2023Review
- Combining Radiological and Genomic TB Portals Data for Drug Resistance Analysis.IEEE access : practical innovations, open solutions · 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
- Generalization Challenges in Drug-Resistant Tuberculosis Detection from Chest X-rays.Diagnostics (Basel, Switzerland) · 2022Article
- The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis.Frontiers in medicine · 2022Review
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Authors and funding
7 authors.
Funding
Abstract
Tuberculosis (TB) is a serious infectious disease that mainly affects the lungs. Drug resistance to the disease makes it more challenging to control. Early diagnosis of drug resistance can help with decision making resulting in appropriate and successful treatment. Chest X-rays (CXRs) have been pivotal to identifying tuberculosis and are widely available. In this work, we utilize CXRs to distinguish between drug-resistant and drug-sensitive tuberculosis. We incorporate Convolutional Neural Network (CNN) based models to discriminate the two types of TB, and employ standard and deep learning based data augmentation methods to improve the classification. Using labeled data from NIAID TB Portals and additional non-labeled sources, we were able to achieve an Area Under the ROC Curve (AUC) of up to 85% using a pretrained InceptionV3 network.
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Registered trials
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