Abstract

AI-derived bone mineral density from routine radiographs showed strong agreement with DXA and comparable ability to predict incident fractures. This opportunistic approach may support osteoporosis screening and early identification of high-risk individuals without reliance on dedicated DXA examinations.

Introduction 

Osteoporosis is a major cause of fragility fractures, yet limited access to DXA leads to underdiagnosis and delayed treatment. Recent advances in artificial intelligence enable extraction of bone structural information from routine radiographs, providing a potential tool for opportunistic osteoporosis screening. Whether AI-derived BMD can approximate DXA and predict real-world fracture risk remains unclear.

Methods

Adults aged ≥ 20 years who underwent both lumbar DXA and radiographic examinations (lumbosacral or kidney-ureter-bladder) within six months between January 2014 and December 2024 were retrospectively analyzed. Lumbar BMD was estimated using DeepXray Spina and compared with DXA using Pearson correlation, intraclass correlation coefficient (ICC), and Bland-Altman analysis. Diagnostic performance for osteoporosis (T-score ≤ – 2.5) and fracture prediction was evaluated using receiver operating characteristic (ROC) analysis, Cohen’s κ, and logistic regression.

Results 

Among 540 participants (73.9% female; mean age 57.0 years; mean follow-up 6.4 years), AI- and DXA-derived BMD showed strong agreement (r = 0.943; ICC = 0.934). For osteoporosis diagnosis, AI-derived T-scores achieved an AUC of 0.959, κ = 0.74, and 90% accuracy. AI- and DXA-derived BMD showed comparable performance for predicting vertebral (AUC 0.704 vs. 0.678) and hip fractures (0.716 vs. 0.678). For all-site fractures, AI-derived BMD showed a modestly higher AUC than DXA-derived BMD (AUC, 0.699 vs 0.677; P = 0.042). Lower AI-derived BMD was independently associated with higher fracture risk.