{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T06:38:04Z","timestamp":1768631884096,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683881","type":"print"},{"value":"9781643683898","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,18]]},"abstract":"<jats:p>Patients with low bone mineral density (BMD) are at risk for fractures however are often undiagnosed. Therefore, there is a need to opportunistically screen for low BMD in patients who present for other studies. This is a retrospective study of 812 patients aged 50 years or older who had dual-energy X-ray absorptiometry (DXA) and radiographs of the hands within 12 months of each other. This dataset was randomly split into training\/validation (n=533) and test (n=136) datasets. A deep learning (DL) framework was used to predict osteoporosis\/osteopenia. Correlations between the textural analysis of the bones and DXA measurements were obtained. We found that the DL model had an accuracy of 82.00%, sensitivity of 87.03%, specificity of 61.00% and an area under the curve (AUC) of 74.00% to detect osteoporosis\/osteopenia. Our findings show that radiographs of the hand can be used to screen for osteoporosis\/osteopenia and identify patients who should get formal DXA evaluation.<\/jats:p>","DOI":"10.3233\/shti230306","type":"book-chapter","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T08:48:40Z","timestamp":1684486120000},"source":"Crossref","is-referenced-by-count":5,"title":["Opportunistic Screening for Osteoporosis Using Hand Radiographs: A Preliminary Study"],"prefix":"10.3233","author":[{"given":"Farid Ghareh","family":"Mohammadi","sequence":"first","affiliation":[{"name":"Department of Radiology, Mayo Clinic, Jacksonville, Fl, 32224, USA"},{"name":"Center for Augmented Intelligence, Mayo Clinic, Jacksonville, Fl, 32224, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronnie","family":"Sebro","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic, Jacksonville, Fl, 32224, USA"},{"name":"Center for Augmented Intelligence, Mayo Clinic, Jacksonville, Fl, 32224, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Caring is Sharing \u2013 Exploiting the Value in Data for Health and Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI230306","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T15:02:40Z","timestamp":1685545360000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI230306"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,18]]},"ISBN":["9781643683881","9781643683898"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti230306","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,18]]}}}