{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T04:59:15Z","timestamp":1785473955755,"version":"3.56.0"},"reference-count":100,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Osteoporosis often remains undetected until fracture. Dual-energy X-ray absorptiometry (DXA) screening is limited by poor accessibility. Opportunistic screening of routine medical images, powered by artificial intelligence (AI), may enable automated, large-scale bone health assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This systematic review followed PRISMA 2020 guidelines. We searched PubMed, Cochrane Library, and Web of Science up to February 2026 for studies developing AI models for opportunistic osteoporosis screening. Two reviewers independently screened, extracted data, and assessed risk of bias using phase classification and PROBAST. Outcomes included osteoporosis, osteopenia, and fracture risk prediction, evaluated mainly by area under the curve (AUC). Principal component analysis (PCA) was used to explore sources of heterogeneity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of 57 included studies (2019\u20132026), most were retrospective and single-center. Computed tomography (CT) was the most common modality (38 studies), and the spine was the most frequent ROI. Deep learning (29 studies) has largely replaced traditional machine learning, and foundation models have recently emerged. Model performance was generally high (AUC range 0.630\u20131.000 across tasks), but heterogeneity was substantial. PCA identified sample size, number of centers, and single-center design as major drivers of heterogeneity, smaller and single-center studies tended to report higher AUCs, suggesting possible overestimation. PROBAST assessment revealed that the absence of external validation was the leading source of bias, only 36% of studies conducted external validation, with additional concerns in participant selection and analysis domains.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI-based opportunistic osteoporosis screening has progressed rapidly with encouraging performance. However, the evidence base remains dominated by retrospective single-center studies with limited external validation, which hinders clinical translation. Future work should prioritize multicenter collaboration, prospective validation, and standardized reporting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Systematic review registration<\/jats:title>\n                    <jats:p>\n                      <jats:ext-link>https:\/\/www.crd.york.ac.uk\/PROSPERO\/view\/CRD420261451716<\/jats:ext-link>\n                      , identifier PROSPERO (CRD420261451716).\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1870486","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T04:24:47Z","timestamp":1785471887000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence for opportunistic screening of osteoporosis across multiple imaging modalities: a systematic review"],"prefix":"10.3389","volume":"13","author":[{"given":"Xiang","family":"Chen","sequence":"first","affiliation":[{"name":"Yuhuan Second People\u2019s Hospital","place":["Taizhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanling","family":"Wu","sequence":"additional","affiliation":[{"name":"Yuhuan Second People\u2019s Hospital","place":["Taizhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianying","family":"Shen","sequence":"additional","affiliation":[{"name":"Yuhuan Second People\u2019s Hospital","place":["Taizhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyan","family":"Chen","sequence":"additional","affiliation":[{"name":"Yuhuan Second People\u2019s Hospital","place":["Taizhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1056\/NEJMcp1513724","article-title":"Clinical practice. 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