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Five occlusal sites per tooth were measured using a spectrophotometer under standardized conditions. Site-level CIELAB values were transformed into engineered color features and aggregated at the tooth level. Teeth were categorised as sound (ICDAS 0), initial carious lesions (1\u20132), and moderate carious lesions (3\u20134). Five ML models: Random Forest (RF), XGBoost, CatBoost, Multilayer Perceptron (MLP), and a Deep Sets architecture were trained. Performance was evaluated using accuracy, balanced accuracy (BA), and macro-F1, with additional binary analyses for early (ICDAS 1\u20134 vs. 0) and operative lesion detection (ICDAS 3\u20134 vs. 0\u20132), based on the included ICDAS range. Learning curve analysis was performed to evaluate the effect of training data size on model performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Deep Sets achieved the highest multiclass performance (BA\u2009=\u20090.89) followed by the MLP (BA\u2009=\u20090.72). Tree-based models demonstrated lower performance overall. For early lesion detection, all models showed high sensitivity (SE) but reduced specificity (SP). At the operative level, the MLP achieved 100% SE and moderate SP. Learning-curve analysis showed that neural models benefited most from increased training data, whereas tree-based models showed limited improvement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Under controlled in-vitro conditions, spectrophotometric CIELAB measurements enabled machine-learning classification of occlusal caries severity. Deep sets achieved the highest overall performance, supporting the potential of color-based approaches as adjunctive tools for caries assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical relevance<\/jats:title>\n                    <jats:p>Spectrophotometric tooth color measurements may support machine learning classification of occlusal caries severity under controlled conditions, using an image-independent approach. Such color-based methods may complement visual assessment by providing a more standardized and reproducible evaluation of enamel changes following further validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00784-026-06911-x","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T03:46:51Z","timestamp":1778730411000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Classification of occlusal caries severity using spectrophotometric CIELAB measurements and machine learning algorithms"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2017-2454","authenticated-orcid":false,"given":"Farah","family":"Rashid","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2004-0531","authenticated-orcid":false,"given":"James","family":"Dudley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"issue":"7","key":"6911_CR1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11071136","volume":"11","author":"DL Duong","year":"2021","unstructured":"Duong DL, Nguyen QDN, Tong MS, Vu MT, Lim JD, Kuo RF (2021) Proof-of-concept study on an automatic computational system in detecting and classifying occlusal caries lesions from smartphone color images of unrestored extracted teeth. 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No personal identifiers, clinical information, or links to patient records were accessible to the authors. In accordance with the National Statement on Ethical Conduct in Human Research (NHMRC, 2007) and the Adelaide University Human Research Ethics guidelines, research using existing collections of fully anonymized human biological materials and involving negligible risk is exempt from the requirement for Human Research Ethics Committee (HREC) review. After completing the institutional Ethics Self- Assessment Checklist, this current project was classified as negligible risk research and formally exempted from HREC review. Therefore, ethics approval was not required under local institutional and national guidelines.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statement of ethics"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"233"}}