Artificial intelligence for opportunistic osteoporosis screening on chest radiographs

Artificial intelligence for opportunistic osteoporosis screening on chest radiographs. Download PDF Abstract We developed an artificial intelligence model (AI-OsRM) to predict osteoporosis and reduced bone mineral density (BMD) from chest radiographs (CXR) and evaluated its performance and clinical utility across three external cohorts. AI-OsRM was developed using 46,478 paired CXR and dual-energy X-ray absorptiometry (DXA) examinations from 26,757 patients collected at a tertiary referral hospital between 2016 and 2022, using a ResNet-based architecture with masked autoencoder pretraining.

External validation was performed in three independent cohorts, including a temporally separated hospital cohort (SNUH; n = 6391) and two large health checkup cohorts (HPC, n = 24,132; YSMC, n = 122,535). In the SNUH cohort, AI-OsRM achieved an area under the receiver operating characteristic curve (AUROC) of 0.88 (95% CI: 0.87–0.89) for both osteoporosis and reduced BMD. In the health checkup cohorts, AUROCs for osteoporosis detection were 0.93 (95% CI: 0.92–0.93) in HPC and 0.92 (95% CI: 0.92–0.92) in YSMC.

Simulation analyses demonstrated that AI-OsRM–based prescreening could reduce DXA utilization by about 50% while maintaining detection of 98-99% of osteoporosis cases and 80–90% of reduced BMD cases. These findings suggest that AI-OsRM can enable accurate identification of individuals with osteoporosis or reduced BMD and may serve as an effective prescreening tool to optimize DXA utilization. Explore related subjects Discover the latest articles and news in related subjects.

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