AI-derived Opportunistic Screening Using Chest Radiographs Identifies Age- and Sex-Associated Bone Mineral Density Patterns and Supports Earlier Osteoporosis Evaluation

AI-derived opportunistic screening using routine chest radiographs was evaluated in 6,028 adults and internally validated against DXA in a subset of participants. AI-derived T-scores identified marked age- and sex-associated skeletal differences, particularly among women aged 50–59 years, a population often underrepresented in current DXA-based screening strategies. Osteoporosis is associated with substantial fracture-related morbidity, yet many individuals with low bone mineral density (BMD) remain undetected during midlife, when routine dual-energy X-ray absorptiometry (DXA) is not commonly performed. We evaluated whether AI-derived opportunistic screening using routine chest radiographs could identify age-associated skeletal patterns and potential high-risk periods for osteoporosis evaluation. In this retrospective cross-sectional study, 6,028 adults aged ≥ 50 years underwent chest radiographs with AI-derived lumbar spine BMD estimation using a validated deep learning model. Participants were stratified by sex and 5-year age cohorts to assess age-associated differences in AI-derived T-scores and osteoporosis prevalence. In an internal validation subset (n = 446), AI-derived T-scores were compared with DXA measurements using correlation, diagnostic performance, and Bland–Altman agreement analyses. The mean AI-derived T-score was − 1.64 ± 1.08; women exhibited significantly lower T-scores than men (− 2.15 ± 0.97 vs. − 1.19 ± 0.96; P < 0.001). Lower mean T-scores and higher osteoporosis prevalence were observed with advancing age in both sexes. Osteoporosis prevalence increased from 10.3