Cost-effectiveness of opportunistic osteoporosis screening using chest radiographs with deep learning in Germany

Osteoporosis is often underdiagnosed due to limitations in traditional screening methods, leading to missed early intervention opportunities. AI-driven screening using chest radiographs could improve early detection, reduce fracture risk, and improve public health outcomes.

To assess the cost-effectiveness of deep learning models (hereafter referred to as AI-driven) applied to chest radiographs for opportunistic osteoporosis screening in German women aged 50 and older.

A decision tree and microsimulation Markov model were used to calculate the cost per quality-adjusted life year (QALY) gained (€2024) for screening with AI-driven chest radiographs followed by treatment, compared to no screening and treatment. Patient pathways were based on AI model accuracy and German osteoporosis guidelines. Women with a fracture risk below 5% received no treatment, those with 5-10% risk received alendronate, and women 65 + with a risk above 10% received sequential treatment starting with romosozumab. Data was validated by a German clinical expert, incorporating real-world treatment persistence, DXA follow-up rates, and treatment initiation. Sensitivity analyses assessed parameter uncertainty.

The cost per QALY gained from screening was €13,340, far below the typical cost-effectiveness threshold of €60,000. Optimizing follow-up, treatment initiation, and medication adherence further improved cost-effectiveness, with dominance achievable by halving medication non-persistence, and in women aged 50-64.

AI-driven chest radiographs for opportunistic osteoporosis screening is a cost-effective strategy for German women aged 50+, with the potential to significantly improve public health outcomes, reduce fracture burdens and address healthcare disparities. Policymakers and clinicians should consider implementing this scalable and cost-effective screening strategy.