{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T04:03:30Z","timestamp":1789445010023,"version":"build-2803163510"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Skeletal Radiol"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and validate an artificial intelligence-based tool for the diagnosis of osteoporosis\/osteopenia using hip radiographs. The tool aims to classify femurs into risk-based categories for osteoporosis\/osteopenia, enabling patient prioritization, enhancing preventive medicine through incidental detection, and assisting clinicians\u2019 diagnosis in general.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>The AI tool was designed to perform three preprocessing tasks before the osteoporosis\/osteopenia prediction: (1) splitting images into single femurs, (2) identifying and discarding femurs with prostheses, and (3) cropping images to isolate the proximal femur. A total of 2691 anteroposterior hip radiographs from 1654 patients were included in the study. The osteoporosis\/osteopenia prediction model was trained on 3227 single femur images and tested on 826. Additionally, a final evaluation experiment was conducted on 313 new radiographs from 239 patients to assess the tool\u2019s applicability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The tool demonstrated high performance in the preprocessing tasks, achieving 99.0% accuracy in classifying single vs. double femur images, 99.3% accuracy in identifying prosthetic femurs, and 99.2% pixel accuracy in delineating the proximal femur before cropping. The final prediction model achieved an area under the curve of 86.6% for detecting osteoporosis\/osteopenia in the test set and 81.0% in the final evaluation experiment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The obtained results demonstrate the potential of the proposed AI-based pipeline for prediction of osteoporosis\/osteopenia using hip radiographs. This study suggests that a tool based on the proposed methods could support DXA triage, incidental osteoporosis detection, and clinical decision-making in settings with limited access to bone densitometry.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00256-025-05033-x","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T12:27:20Z","timestamp":1758889640000},"page":"415-424","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An AI-based pipeline for osteoporosis\/osteopenia prediction using hip radiographs"],"prefix":"10.1007","volume":"55","author":[{"given":"Jos\u00e9","family":"Acosta-Batlle","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1607-5130","authenticated-orcid":false,"given":"David","family":"Coronado-Guti\u00e9rrez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javier","family":"Soto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaime","family":"Moujir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos","family":"L\u00f3pez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos","family":"Suevos Ballesteros","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M\u00f3nica","family":"V\u00e1zquez D\u00edaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mar\u00eda del Prado","family":"Ordu\u00f1a D\u00edez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Fern\u00e1ndez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javier","family":"Bl\u00e1zquez S\u00e1nchez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"key":"5033_CR1","doi-asserted-by":"publisher","first-page":"46","DOI":"10.5152\/eurjrheum.2016.048","volume":"4","author":"T Sozen","year":"2017","unstructured":"Sozen T, Ozisik L, Calik Basaran N. 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The institutional review board of Hospital Universitario Ram\u00f3n y Cajal in Madrid (Spain) approved this study (code 329\u201321). Due to the study\u2019s retrospective design, the institutional review board waived the need for informed patient consent to participate.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"David Coronado-Guti\u00e9rrez, Carlos L\u00f3pez, and David Fern\u00e1ndez are employees of Transmural Biotech S.L. The remaining authors have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}