Advancing diagnostic equity through artificial intelligence chest radiograph screening for osteoporosis in Asian populations.
Authors
Affiliations (10)
Affiliations (10)
- Department of Family Medicine, St. Paul's Hospital, Taoyuan, Taiwan.
- Health Management Center, St. Paul's Hospital, Taoyuan, Taiwan.
- Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei, Taiwan.
- Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei, Taiwan. [email protected].
- Acer Medical Inc., New Taipei City, Taiwan.
- Acer Inc., Taipei City, Taiwan.
- Information Technology Department, St. Paul's Hospital, Taoyuan, Taiwan.
- Department of Orthopedic Surgery, Taichung Veterans General Hospital, Taichung, Taiwan. [email protected].
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan. [email protected].
- Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan. [email protected].
Abstract
Early identification of abnormal bone mineral density (BMD) through opportunistic screening is critical for preventing osteoporotic fractures. We validated an AI model in 2384 asymptomatic adults (57.7% female; mean age 43.6 years) undergoing health examinations in Taiwan. Using DXA as the reference, the model identified 255 suspected abnormal BMD cases, with 94 (3.9%) DXA-confirmed positive. Population-level performance was robust, yielding an AUC of 0.95 (95% CI 0.93-0.99) and sensitivity of 79.7% (95% CI 71.3-86.5%). Although BMI distributions paralleled East Asian regional trends, intersectional subgroup analyses remain exploratory due to small event counts. Decision curve analysis indicated superior net benefit for AI-based referral over "refer all" or "refer none" strategies, particularly for women with normal BMI (18.5-23 kg/m²). This AI tool offers precise triage for Asian health examination populations, though further validation in multi-center cohorts is required to confirm broad generalizability.