{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:36:44Z","timestamp":1792787804786,"version":"4.5.0"},"reference-count":30,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Jiangsu Province Hospital High-level Talent Cultivation Program","award":["CZ0121002010039"],"award-info":[{"award-number":["CZ0121002010039"]}],"id":[{"id":"https:\/\/ror.org\/04py1g812","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Natural Science Foundation of China","award":["82272499"],"award-info":[{"award-number":["82272499"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Global Spine Journal"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>Retrospective Cohort Study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study explores using deep learning to predict lumbar spine bone mineral density (BMD) from CT images, aiming to improve osteoporosis and osteopenia detection and facilitate early intervention. While dual-energy X-ray absorptiometry (DEXA) is the gold standard, it often overestimates BMD in skeletal hyperostosis. We sought to develop a CT-based model capable of accurately predicting trabecular BMD and validating its fracture-risk prediction performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study analyzed 1840 lumbar vertebrae (L1\u2013L4) from 460 patients with available DEXA results. Patients were randomly split 7:3 (patient-level) into training and test cohorts. Trabecular regions were semi-automatically segmented, and based on DEXA, we developed a regression model for predicting BMD values. Additionally, a three-class deep learning model classified normal bone mass, osteopenia, and osteoporosis. Next, we established a BMD\u2013fracture risk prediction model in patients without lumbar osteophytosis or ligamentous calcification and externally validated its performance. In patients with lumbar osteophytosis or ligamentous calcification, we compared fracture-risk prediction between DEXA and model-predicted BMD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Model-predicted BMD correlated strongly with DEXA (r = 0.934). The three-class model achieved AUCs of 0.90 (normal), 0.79 (osteopenia), and 0.92 (osteoporosis). External validation confirmed that model-predicted BMD (AUC = 0.901) outperformed DEXA (AUC = 0.464) in fracture-risk prediction among hyperostotic patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Deep learning enabled accurate BMD estimation from lumbar CT images, supporting osteoporosis diagnosis and fracture-risk stratification. Notably, in patients with lumbar osteophytosis or ligamentous calcification, CT-based predicted BMD provided superior risk discrimination compared with DEXA, indicating closer alignment with actual bone status.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/21925682261429415","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T12:49:51Z","timestamp":1772023791000},"page":"3258-3268","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning-Based Prediction of Trabecular Bone Mineral Density From Lumbar CT: A Superior Alternative to DEXA in Hyperostosis"],"prefix":"10.1177","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2646-9977","authenticated-orcid":false,"given":"Jiayang","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Orthopedics"},{"name":", The First Affiliated Hospital of Nanjing Medical 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