Cost-utility analysis of deep learning-based opportunistic osteoporosis screening using chest radiographs: implications of diagnostic thresholds and confirmatory testing

Osteoporosis often goes undetected until a fracture occurs. Using a deep learning model to analyze routine chest radiographs may cost-effectively identify at-risk individuals. Economic benefit depends on how common the disease is, how the model is calibrated, and whether patients follow through with confirmatory bone density testing and pharmacological treatment initiation.

Background

Osteoporosis is common among postmenopausal women and often unrecognized until fragility fractures occur, despite its clinical and economic burden. Deep learning (DL)-based opportunistic screening using existing chest radiographs is a potential approach, but its economic value may vary with disease prevalence, diagnostic performance, and care pathway completion.