Pulmonary tuberculosis prediction using CAD4TB artificial intelligence (computer-aided detection for tuberculosis) based on thoracic x-ray photos among Indonesian subjects in hospital - Data for Publication

Tuberculosis is still one of the world’s leading infectious diseases, especially in countries like Indonesia where many cases go undetected due to limited access to expert medical imaging professionals. In this study, we explored whether a computer system called CAD4TB, which uses artificial intelligence to read chest X-rays, could help identify people with tuberculosis in a hospital setting. We compared its performance to experienced radiologists and used laboratory tests to confirm the results. We found that CAD4TB was able to detect tuberculosis with similar accuracy to human experts in many cases, especially among patients without complications like fluid in the lungs. Although expert radiologists remain slightly more accurate overall, CAD4TB performed well enough to suggest that it could be used to support hospital teams—especially where there are not enough trained readers. Because this system can analyze X-rays quickly and consistently, we believe it could be useful in busy hospitals and in regions with high numbers of tuberculosis cases. Our findings may help health programs consider how digital tools like CAD4TB can be integrated into screening and diagnosis strategies to improve early detection and treatment.