Advancing Osteoporosis Detection Through AI-Driven CT Segmentation: Insights from Multiple Large Health System Biobanks

<title>Abstract</title> <p>Low bone density (LBD), defined as the combined group of osteoporosis and osteopenia, remains substantially underdiagnosed despite the presence of effective screening and treatment options. Our study evaluates whether fully automated artificial intelligence-based segmentation of L1 vertebral body attenuation on computed tomography (CT) enables scalable opportunistic LBD screening across two large academic institutions. Overlap between CT-defined LBD and ICD-coded diagnoses was limited, with 59–76% of imaging-defined cases lacking a corresponding clinical diagnosis, indicating a large undetected population. Phenome-wide association studies in both cohorts linked LBD to osteoporosis, respiratory failure, sepsis, coagulation defects, and cardiovascular disease, while genome-wide analysis identified no significant loci. Additionally, median L1 vertebral attenuation declined most sharply between the fifth and sixth decades, indicative of a potentially earlier decline of bone density than the start of current gold standard osteoporosis screening. Combined, these findings support automated opportunistic CT analysis as a feasible, scalable strategy for earlier LBD detection and for revealing clinically relevant population-level associations.</p>