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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To assess the economic value of a DL-based opportunistic osteoporosis screening approach among Taiwanese postmenopausal women.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11657-026-01788-5","type":"journal-article","created":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T17:59:20Z","timestamp":1792778360000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cost-utility analysis of deep learning-based opportunistic osteoporosis screening using chest radiographs: implications of diagnostic thresholds and confirmatory testing"],"prefix":"10.1007","volume":"21","author":[{"given":"Bo-Xiang","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dung-Jang","family":"Tsai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chin","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shih-Hua","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping-Hsuan","family":"Hsieh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen-Hui","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,23]]},"reference":[{"issue":"6","key":"1788_CR1","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1001\/jama.285.6.785","volume":"285","author":"NIH Consensus Development Panel on","year":"2001","unstructured":"NIH Consensus Development Panel on (2001) Osteoporosis prevention, diagnosis, and therapy. 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