Cost-Effectiveness of Opportunistic Osteoporosis Screening Using Chest Radiographs With Deep Learning in the United
Mickael Hiligsmann1, Stuart L Silverman2, Jean-Yves Reginster3
1Professor of Health Preferences and Economics of Prevention, Department of Health Services Research, Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands.
Opportunistic osteoporosis screening using deep learning on chest X-rays is cost-effective for women aged 50+, costing $72,085 per QALY gained. This AI-driven approach improves health outcomes and addresses unmet needs in osteoporosis diagnosis.
Area of Science:
- Radiology and Medical Imaging
- Artificial Intelligence in Healthcare
- Public Health and Epidemiology
Background:
- Opportunistic osteoporosis screening using deep learning on chest radiographs shows promise for early detection in middle-aged and older adults.
- Existing chest X-rays can be leveraged for osteoporosis screening, reducing the need for dedicated imaging.
- Artificial intelligence (AI) enhances the diagnostic capabilities of radiographs for osteoporosis detection.
Purpose of the Study:
- To evaluate the cost-effectiveness of opportunistic osteoporosis screening using deep learning on chest radiographs in US women aged 50 years and over.
- To compare the economic impact of AI-driven screening followed by treatment versus no screening.
- To assess the public health value and potential for improving osteoporosis care.
Main Methods:
- An economic model combining decision tree and Markov microsimulation was used to estimate the cost per quality-adjusted life-year (QALY) gained.
- The model incorporated AI-enhanced radiograph sensitivity and specificity, real-world medication persistence, and treatment initiation rates.
- Patient pathways included risk stratification for osteoporosis treatment with alendronate or abaloparatide, with sensitivity analyses performed.
Main Results:
- Opportunistic AI-driven screening improved health outcomes, increasing QALYs and reducing fractures, despite higher treatment costs.
- The estimated cost per QALY gained was $72,085, which is below the US cost-effectiveness threshold of $100,000.
- Optimizing follow-up, treatment initiation, and medication adherence can further enhance cost-effectiveness.
Conclusions:
- Opportunistic AI-driven osteoporosis screening using chest radiographs is a cost-effective strategy with significant public health value.
- This approach demonstrates potential for improving early osteoporosis detection and addressing unmet needs in patient care.
- Integrating AI into existing imaging workflows offers a viable solution for widespread osteoporosis screening.
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