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Quantitative light-induced fluorescence (QLF) images from 104 adults (20\u201370 years; 2,733 teeth) were analyzed. To prevent data leakage, the dataset was split at the participant level. A random forest (RF) regressor was optimized, and recursive feature elimination with cross-validation (RFECV) identified efficient tooth subsets. Final models were validated using an independent test set, and correlations between mean \u0394F\n                    <jats:sub>wear<\/jats:sub>\n                    and chronological age were assessed. Cross-validation (CV) performance peaked with three teeth; however, independent testing showed that a model incorporating seven key teeth achieved the best generalization performance. This 7-tooth model achieved a mean absolute error (MAE) of 7.49 years (95% CI: 5.90\u20139.17), comparable to the full 28-tooth model (MAE: 7.27 years;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.79), with a stronger Pearson correlation with age (\n                    <jats:italic>r<\/jats:italic>\n                    \u2009=\u20090.78 vs. 0.71) and an equivalent R\n                    <jats:sup>2<\/jats:sup>\n                    of 0.61. These findings support the feasibility of integrating \u0394F\n                    <jats:sub>wear<\/jats:sub>\n                    with an interpretable machine-learning framework for non-invasive age estimation. While the reduced 7-tooth model offers analytical efficiency, further validation in larger and more diverse cohorts is required to confirm its generalizability for broader forensic or epidemiological applications.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-42573-1","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T17:05:58Z","timestamp":1775149558000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AI-assisted age estimation from occlusal tooth wear using biofluorescence imaging"],"prefix":"10.1038","volume":"16","author":[{"given":"Sang-Kyeom","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eun-Song","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baek-Il","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"key":"42573_CR1","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.forsciint.2006.05.016","volume":"165","author":"A Schmeling","year":"2007","unstructured":"Schmeling, A., Geserick, G., Reisinger, W. & Olze, A. 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