{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:42:42Z","timestamp":1767339762944},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685335","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"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":[[2024,8,22]]},"abstract":"<jats:p>Patients with low bone mineral density (BMD) face an increased risk of fractures, yet are frequently undiagnosed. Consequently, it is imperative to have opportunistically screen for low BMD in patients undergoing other medical evaluations. This retrospective study encompassed 422 patients aged \u2265 50 who underwent both dual-energy X-ray absorptiometry (DXA) and hand radiographs (modality of digital X-ray) from three different vendors within a 12-month period. The dataset was randomly divided into training\/validation (n=338) and test (n=84) datasets. we sought to predict osteoporosis\/osteopenia and establish correlations between bone textural analysis and DXA measurements. Our results demonstrate that the deep learning model achieved an accuracy of 77.38%, sensitivity of 77.38%, specificity of 73.63%, and an area under the curve (AUC) of 83% in detecting osteoporosis\/osteopenia. These findings suggest that hand radiographs can serve as a viable screening tool for identifying individuals warranting formal DXA assessment for osteoporosis\/osteopenia.<\/jats:p>","DOI":"10.3233\/shti240411","type":"book-chapter","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:06:00Z","timestamp":1724403960000},"source":"Crossref","is-referenced-by-count":1,"title":["Opportunistic Screening for Osteoporosis Using Hand Radiographs \u2013 A Feature Augmentation Study Technique (FAST)"],"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","Digital Health and Informatics Innovations for Sustainable Health Care Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240411","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:06:01Z","timestamp":1724403961000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240411"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"ISBN":["9781643685335"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240411","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}