Artificial Intelligence-enabled Electrocardiography Detects Osteoporosis with chest X-ray and identifies the mortality events

Abstract SUMMARY A deep learning model was developed to identify osteoporosis from chest X-ray features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This AI-enabled chest X-ray strategy may function as an early detection screening tool for osteoporosis. OBJECTIVE The aim of this study was to develop a deep learning model (DLM) to identify osteoporosis via chest X-ray features and investigate the performance and clinical implications. METHOD This study collected 48,353 CXRs with the corresponding T score according to DXA from the academic medical center. Among these, 35,633 CXRs were used to identify CXR-OP. Another 12,720 CXRs were used to validate the performance, which was evaluated by the area under the receiver operating characteristic curve (AUC). Furthermore, CXR-OP was tested to assess the long-term risks of mortality, which were evaluated by Kaplan‒Meier survival analysis and the Cox proportional hazards model. RESULTS The DLM utilizing CXR achieved AUCs of 0.930 and 0.892 during internal and external validation, respectively. The group that underwent DXA with CXR-OP had a higher risk of all-cause mortality (hazard ratio [HR] 2.59, 95% CI: 1.83–3.67), and those classified as CXR-OP in the group without DXA also had higher all-cause mortality (HR: 1.67, 95% CI: 1.61–1.72) in the internal validation set. The external validation set produced similar results. CONCLUSION Our DLM uses chest X-rays for early detection of osteoporosis, aiding physicians to identify those at risk. It has significant prognostic implications, improving life quality and reducing mortality. AI-enabled CXR strategy may serve as a screening tool.