Cost-utility analysis of deep learning-based opportunistic osteoporosis screening using chest radiographs: implications of diagnostic thresholds and confirmatory testing.
10 affiliations
- 1School of Pharmacy, College of Pharmacy, National Defense Medical University, Taipei, Taiwan, ROC.
- 2Department of Pharmacy Practice, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan, ROC.
- 3Digital Medical Center, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan, ROC.
- 4School of Medicine, College of Medicine, National Defense Medical University, Taipei, Taiwan, ROC.
- 5Division of Nephrology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan, ROC.
- 6Department of Nephrology, School of Medicine, College of Medicine, National Defense Medical University, Taipei, Taiwan, ROC.
- 7School of Pharmacy, College of Pharmacy, National Defense Medical University, Taipei, Taiwan, ROC. [email protected].
- 8Digital Medical Center, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan, ROC. [email protected].
- 9School of Medicine, College of Medicine, National Defense Medical University, Taipei, Taiwan, ROC. [email protected].
- 10Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan, ROC. [email protected].
Abstract
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.Aim
To assess the economic value of a DL-based opportunistic osteoporosis screening approach among Taiwanese postmenopausal women.Methods
A cost-utility analysis was conducted from the Taiwan healthcare perspective using a hybrid decision tree and Markov model. The model simulated a cohort of 50-year-old postmenopausal women over a 50-year horizon. Model inputs, including diagnostic performance, long-term outcomes, costs, and utilities, were derived from published literature, preferably local sources. Additional analyses examined alternative prevalence settings, sensitivity and specificity combinations, and confirmatory DXA and treatment initiation rates.Results
In the base case, assuming full care-pathway completion, DL-based screening was cost-effective versus no screening, with an ICER of 21,827 per QALY, below the willingness-to-pay threshold of one GDP per capita in Taiwan. The approach became cost-effective when osteoporosis prevalence exceeded 14.3%. Across the diagnostic continuum, a sensitivity of 86.6% and specificity of 76.3% generated the greatest economic benefit.Conclusion
DL-based opportunistic osteoporosis screening was cost-effective under full care-pathway implementation. Its value depended on disease prevalence, diagnostic performance, and completion of confirmatory DXA and treatment initiation, and was not maintained under real-world examination estimates observed in Taiwan. Strengthening the post-screening care pathway is therefore central to realizing this value.
Related
- Real-World Validation of an AI-Based Opportunistic Osteoporosis Risk Stratification Tool Using Chest Radiographs in a Male-Predominant Cohort.
- Federated learning for privacy-preserving opportunistic screening of lumbar-spine DXA-defined osteoporotic-level bone mineral density using chest radiographs: A multi-center independent validation study
- AI-Driven opportunistic osteoporosis screening: the skeletal failure paradigm for trustworthy vertebral fracture triage
- Confirmatory biomarker diagnostic studies are not needed when transitioning from NAFLD to MASLD
- Opportunistic computed tomography to identify osteoporosis in primary hyperparathyroidism using sestamibi SPECT/CT scans
BibTeX
@article{shi2026costutility,
title = {{Cost-utility analysis of deep learning-based opportunistic osteoporosis screening using chest radiographs: implications of diagnostic thresholds and confirmatory testing.}},
author = {Bo-Xiang Shi and Dung-Jang Tsai and Chin Lin and Shih-Hua Lin and Ping-Hsuan Hsieh and Wen-Hui Fang},
journal = {Archives of osteoporosis},
year = {2026},
month = oct,
doi = {10.1007/s11657-026-01788-5},
url = {https://europepmc.org/article/MED/42920040},
note = {CC BY},
}
RIS
TY - JOUR
TI - Cost-utility analysis of deep learning-based opportunistic osteoporosis screening using chest radiographs: implications of diagnostic thresholds and confirmatory testing.
AU - Bo-Xiang Shi
AU - Dung-Jang Tsai
AU - Chin Lin
AU - Shih-Hua Lin
AU - Ping-Hsuan Hsieh
AU - Wen-Hui Fang
PY - 2026
DA - 2026/10/23
JO - Archives of osteoporosis
DO - 10.1007/s11657-026-01788-5
UR - https://europepmc.org/article/MED/42920040
AB - 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.Aim To assess the economic value of a DL-based opportunistic osteoporosis screening approach among Taiwanese postmenopausal women.Methods A cost-utility analysis was conducted from the Taiwan healthcare perspective using a hybrid decision tree and Markov model. The model simulated a cohort of 50-year-old postmenopausal women over a 50-year horizon. Model inputs, including diagnostic performance, long-term outcomes, costs, and utilities, were derived from published literature, preferably local sources. Additional analyses examined alternative prevalence settings, sensitivity and specificity combinations, and confirmatory DXA and treatment initiation rates.Results In the base case, assuming full care-pathway completion, DL-based screening was cost-effective versus no screening, with an ICER of 21,827 per QALY, below the willingness-to-pay threshold of one GDP per capita in Taiwan. The approach became cost-effective when osteoporosis prevalence exceeded 14.3%. Across the diagnostic continuum, a sensitivity of 86.6% and specificity of 76.3% generated the greatest economic benefit.Conclusion DL-based opportunistic osteoporosis screening was cost-effective under full care-pathway implementation. Its value depended on disease prevalence, diagnostic performance, and completion of confirmatory DXA and treatment initiation, and was not maintained under real-world examination estimates observed in Taiwan. Strengthening the post-screening care pathway is therefore central to realizing this value.
SN - 1862-3522
SN - 1862-3514
ER -