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We developed and validated a deep learning model that generates accurate dual-energy X-ray absorptiometry (DXA) scans from three-dimensional optical body scans (3DO), enabling compositional analysis of the whole body and specified subregions. Previous works on generative medical imaging models lack quantitative validation and only report quality metrics.<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Methods<\/jats:bold><\/jats:title><jats:p>Our model was self-supervised pretrained on two large clinical DXA datasets and fine-tuned using the Shape Up! Adults study dataset. Model-predicted scans from a holdout test set were evaluated using clinical commercial DXA software for compositional accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Results<\/jats:bold><\/jats:title><jats:p>Predicted DXA scans achieve<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>of 0.73, 0.89, and 0.99 and RMSEs of 5.32, 6.56, and 4.15\u2009kg for total fat mass (FM), fat-free mass (FFM), and total mass, respectively. Custom subregion analysis results in<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>s of 0.70\u20130.89 for left and right thigh composition. We demonstrate the ability of models to produce quantitatively accurate visualizations of soft tissue and bone, confirming a strong relationship between body shape and composition.<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Conclusions<\/jats:bold><\/jats:title><jats:p>This work highlights the potential of generative models in medical imaging and reinforces the importance of quantitative validation for assessing their clinical utility.<\/jats:p><\/jats:sec>","DOI":"10.1038\/s43856-024-00434-w","type":"journal-article","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T05:39:24Z","timestamp":1706593164000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Generative deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using 3D surface scans"],"prefix":"10.1038","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2927-8458","authenticated-orcid":false,"given":"Lambert T.","family":"Leong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael C.","family":"Wong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong E.","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7217-9749","authenticated-orcid":false,"given":"Yannik","family":"Glaser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brandon K.","family":"Quon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nisa N.","family":"Kelly","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Devon","family":"Cataldi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7354-5461","authenticated-orcid":false,"given":"Peter","family":"Sadowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1127-9425","authenticated-orcid":false,"given":"Steven B.","family":"Heymsfield","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2280-2541","authenticated-orcid":false,"given":"John A.","family":"Shepherd","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,30]]},"reference":[{"key":"434_CR1","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1385\/JCD:6:2:75","volume":"6","author":"CV Albanese","year":"2003","unstructured":"Albanese, C. 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