Artificial Intelligence-Enabled Electrocardiography Identifies Osteoporosis and has Prognostic Value.

Background: Osteoporosis, a common disease leading to weakened bones and increased fracture risk, often goes undiagnosed until a fracture occurs. Dual-energy X-ray absorptiometry (DXA) is the current gold standard for bone mineral density (BMD) measurement, but it has limitations. Recent studies reported Artificial Intelligence (AI)-enabled Electrocardiography (ECG) for disease screening. We hypothesized that AI ECG could serve as a screening tool for osteoporosis. Objective: This study aimed to develop a deep learning model (DLM) to identify osteoporosis using EKG features and to assess its performance and clinical implications. Methods: We conducted a retrospective study involving 25,401 patients who underwent 44,732 EKGs with DXA-measured BMD at two hospitals. The area under the receiver operating characteristic curve (AUC) was used for evaluation. Additionally, our DLM was tested for predicting mortality using Kaplan-Meier survival analysis and the Cox proportional hazards model. Results: The DLM achieved an AUC of 0.741 in internal validation and 0.868 in external validation for detecting osteoporosis. Furthermore, the negative predictive value for osteoporosis was 93.7% in the internal set and 85.8% in the external set. The DLM-detected osteoporosis group exhibited a higher risk of all-cause mortality with a hazard ratio (HR) of 2.06 (95% CI: 1.23–3.45) in the internal validation set, and similar results were observed in the external validation set (HR: 1.87, 95% CI: 1.21–2.89). Conclusion: Our DLM, utilizing EKG for osteoporosis identification, demonstrated impressive results. It has the potential to serve as a cost-effective and practical screening tool for early osteoporosis detection, with significant prognostic implications.