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.