{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:31:09Z","timestamp":1760059869201,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T00:00:00Z","timestamp":1752624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Vertebral degenerative features are promising but often subjectively scored indicators for adult age estimation. We evaluated an objective surface roughness metric, the \u201caverage distance to the fitted ellipse\u201d score (DS), calculated automatically for every vertebra from C7 to S1 on routine CT images. Methods: CT scans of 176 adults (94 males, 82 females; 21\u201394 years) were retrospectively analyzed. For each vertebra, the mean orthogonal deviation of the anterior superior endplate from an ideal ellipse was extracted. Sex-specific multiple linear regression served as a baseline; support vector regression (SVR), random forest (RF), k-nearest neighbors (k-NN), and Gaussian na\u00efve-Bayes pseudo-regressor (GNB-R) were tuned with 10-fold cross-validation and evaluated on a 20% hold-out set. Performance was quantified with the standard error of the estimate (SEE). Results: DS values correlated moderately to strongly with age (peak r = 0.60 at L3\u2013L5). Linear regression explained 40% (males) and 47% (females) of age variance (SEE \u2248 11\u201312 years). Non-parametric learners improved precision: RF achieved an SEE of 8.49 years in males (R2 = 0.47), whereas k-NN attained 10.8 years (R2 = 0.45) in women. Conclusions: Automated analysis of vertebral cortical roughness provides a transparent, observer-independent means of estimating adult age with accuracy approaching that of more complex deep learning pipelines. Streamlining image preparation and validating the approach across diverse populations are the next steps toward forensic adoption.<\/jats:p>","DOI":"10.3390\/diagnostics15141794","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T15:48:22Z","timestamp":1752680902000},"page":"1794","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Analysis of Vertebral Body Surface Roughness for Adult Age Estimation: Ellipse Fitting and Machine-Learning Approach"],"prefix":"10.3390","volume":"15","author":[{"given":"Erhan","family":"Kartal","sequence":"first","affiliation":[{"name":"Department of Forensic Medicine, Van Y\u00fcz\u00fcnc\u00fc Y\u0131l University, Van 65090, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7369-6083","authenticated-orcid":false,"given":"Yasin","family":"Etli","sequence":"additional","affiliation":[{"name":"Department of Forensic Medicine, Van Y\u00fcz\u00fcnc\u00fc Y\u0131l University, Van 65090, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.forsciint.2018.03.047","article-title":"Age estimation of adult human remains from hip bones using advanced methods","volume":"287","author":"Navega","year":"2018","journal-title":"Forensic Sci. 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