{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:02:49Z","timestamp":1781762569042,"version":"3.54.5"},"reference-count":53,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal Orthopaedic Research"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:label\/><jats:p>The early detection and treatment of osteoporosis and prevention of fragility fractures are urgent societal issues. We developed an artificial intelligence\u2010assisted diagnostic system that estimated not only lumbar bone mineral density but also femoral bone mineral density from anteroposterior lumbar X\u2010ray images. We evaluated the performance of lumbar and femoral bone mineral density estimations and the osteoporosis classification accuracy of an artificial intelligence\u2010assisted diagnostic system using lumbar X\u2010ray images from a population\u2010based cohort. The artificial neural network consisted of a deep neural network for estimating lumbar and femoral bone mineral density values and classifying lumbar X\u2010ray images into osteoporosis categories. The deep neural network was built by training dual\u2010energy X\u2010ray absorptiometry\u2010derived lumbar and femoral bone mineral density values as the ground truth of the training data and preprocessed X\u2010ray images. Five\u2010fold cross\u2010validation was performed to evaluate the accuracy of the estimated BMD. A total of 1454 X\u2010ray images from 1454 participants were analyzed using the artificial neural network. For the bone mineral density estimation performance, the mean absolute errors were 0.076\u2009g\/cm<jats:sup>2<\/jats:sup> for the lumbar and 0.071\u2009g\/cm<jats:sup>2<\/jats:sup> for the femur between dual\u2010energy X\u2010ray absorptiometry\u2010derived and artificial intelligence\u2010estimated bone mineral density values. The classification performances for the lumbar and femur of patients with osteopenia, in terms of sensitivity, were 86.4% and 80.4%, respectively, and the respective specificities were 84.1% and 76.3%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Clinical Significance<\/jats:title><jats:p>The system was able to estimate the bone mineral density and classify the osteoporosis category of not only patients in clinics or hospitals but also of general inhabitants.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jor.70000","type":"journal-article","created":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T12:01:22Z","timestamp":1752062482000},"page":"1619-1631","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Development of Artificial Intelligence\u2010Assisted Lumbar and Femoral BMD Estimation System Using Anteroposterior Lumbar X\u2010Ray 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