ArticleJMIR formative research2024
Comparing the Output of an Artificial Intelligence Algorithm in Detecting Radiological Signs of Pulmonary Tuberculosis in Digital Chest X-Rays and Their Smartphone-Captured Photos of X-Ray Films: Retrospective Study.
Article in JMIR formative research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Computer-Aided Analysis of Photographed Chest X-Ray Films Performs Well Compared With Trained Radiologists.Mayo Clinic proceedings. Digital health · 2026Article
- Tuberculosis: So much accomplished and so much more to be done.The Indian journal of medical research · 2026Article
- Evaluating the Usefulness of Artificial Intelligence-based Chest X-Ray Screening in Improving Tuberculosis Detection Among the High-Risk Tribal Population of Chhattisgarh, India: A Prospective Multi-Centre Study.Open forum infectious diseases · 2026Article
- Artificial intelligence for tuberculosis management in Africa: opportunities, challenges, and implementation.Frontiers in public health · 2026Review
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6 authors.
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Abstract
backgroundArtificial intelligence (AI) based computer-aided detection devices are recommended for screening and triaging of pulmonary tuberculosis (TB) using digital chest x-ray (CXR) images (soft copies). Most AI algorithms are trained using input data from digital CXR Digital Imaging and Communications in Medicine (DICOM) files. There can be scenarios when only digital CXR films (hard copies) are available for interpretation. A smartphone-captured photo of the digital CXR film may be used for AI to process in such a scenario. There is a gap in the literature investigating if there is a significant difference in the performance of AI algorithms when digital CXR DICOM files are used as input for AI to process as opposed to photos of the digital CXR films being used as input.
objectiveThe primary objective was to compare the agreement of AI in detecting radiological signs of TB when using DICOM files (denoted as CXR
methodsPairs of CXR
resultsA total of 1278 CXR pairs were analyzed. The positive percent agreement of AI was found to be 92.22% (95% CI 89.94-94.12) and 90.75% (95% CI 88.32-92.82), respectively, for CXR
conclusionsWe did not observe any statistically significant differences in the output of AI in digital CXRs and photos of digital CXR films.
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