{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T11:50:49Z","timestamp":1785325849025,"version":"3.55.0"},"reference-count":71,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Microscopy"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Dual\u2010energy X\u2010ray absorptiometry (DXA) remains the clinical gold standard for assessing bone mineral density (BMD), guiding diagnosis and therapeutic decisions. However, conventional DXA analysis suffers from several limitations, including insensitivity to early microarchitectural changes, operator dependence, limited availability in primary care settings, and an insufficient ability to predict fracture risk when used alone. Artificial intelligence (AI), incorporating machine learning (ML) and deep learning (DL), offers transformative potential in enhancing DXA\u2010based bone health assessment. The purpose of this review is to describe the integration of AI algorithms into DXA image interpretation, highlighting improvements in diagnostic accuracy, and fracture risk stratification beyond traditional methods. Using AI\u2010driven models, complex features of DXA images can be extracted, increasing sensitivity to microstructural deterioration that is typically not detected by standard BMD measurements. A combination of quantitative image features and comprehensive demographic and clinical data enhance the early detection of osteoporosis and fracture susceptibility, enabling personalised treatment strategies. In comparison to classical DXA or fracture risk assessment tool (FRAX) algorithms, convolutional neural networks (CNNs) and ensemble methods demonstrate superior predictive performance, with average area under the curve (AUC) values often about 0.90. In addition to minimising inter\u2010operator variability and improving reproducibility, AI improves DXA technical challenges such as region\u2010of\u2010interest selection and image segmentation. In addition to providing indirect measurements of bone microarchitecture, AI\u2010enabled indices, such as the trabecular bone score (TBS), contribute to the improvement of fracture risk prediction. It has been demonstrated in large\u2010scale clinical validations that AI\u2010assisted DXA can enhance bone health diagnostic capability.<\/jats:p>","DOI":"10.1111\/jmi.70140","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T13:42:54Z","timestamp":1782740574000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["The future of DXA: How AI is transforming bone health diagnostics"],"prefix":"10.1111","author":[{"given":"Mahsa","family":"Shakeri","sequence":"first","affiliation":[{"name":"Department of Radiology and Radiotherapy School of Allied Medical Sciences Tehran University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyyed Mohammad","family":"Hosseini","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Biomedical Engineering School of Medicine Tehran University of Medical Sciences  Tehran Iran"},{"name":"Research Center for Molecular and Cellular Imaging (RCMCI) Advanced Medical Technologies and Equipment Institute (AMTEI) Tehran University of Medical Sciences Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikolas","family":"Knowles","sequence":"additional","affiliation":[{"name":"Department of Kinesiology and Health Sciences Faculty of Health University of Waterloo  Waterloo Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Forough","family":"Jamil","sequence":"additional","affiliation":[{"name":"Department of Radiology Faculty of Medicine Hormozgan University of Medical Sciences  Bandar Abbas Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Haghparast","sequence":"additional","affiliation":[{"name":"Department of Radiology Faculty of ParaMedicine Hormozgan University of Medical Sciences  Bandar Abbas Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leili","family":"Darvish","sequence":"additional","affiliation":[{"name":"Department of Radiology Faculty of ParaMedicine Hormozgan University of Medical Sciences  Bandar Abbas Iran"},{"name":"Mother and Child Welfare Research Center Hormozgan University of Medical Sciences  Bandar Abbas Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,29]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cmpb.2019.03.011","article-title":"Computer\u2010aided osteoporosis detection from DXA imaging","volume":"173","author":"Hussain D.","year":"2019","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"e_1_2_10_3_1","unstructured":"International Osteoporosis Foundation. 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