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Clinical Relevance. AI-based tools for caries detection are becoming common in dentistry. This study shows that how these models are evaluated can significantly impact perceived performance. Clinicians and developers should ensure that evaluation standards are independent and clinically relevant to avoid overestimating AI\u2019s diagnostic abilities and to build trust for real-world use and regulatory approval.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Multiple AI caries lesion segmentation models were trained on the ACTA-DIRECT dataset using annotations from (1) single dentists, (2) aggregated strategies (majority vote, consensus meetings, STAPLE), and (3) micro-CT-based methods. Model accuracy was evaluated using two approaches: (1) comparison against micro-CT-based annotations and (2) comparison against the training-matched annotations. Statistical significance of differences in model diagnostic accuracy across annotation strategies was assessed using the McNemar test.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>There was no statistically significant difference in diagnostic accuracy among AI models when compared to micro-CT-based annotations. However, the diagnostic accuracy was considered statistically significantly higher when the results of the AI models were evaluated with the training-matched annotations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our findings indicate a strong influence of reference standards on AI model evaluation. While annotation strategies during training did not significantly affect AI accuracy in caries lesion segmentation, evaluation was subject to bias when models were tested against different reference standards.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical relevance\u00a0<\/jats:title>\n                    <jats:p>AI-based tools for caries detection are becoming common in dentistry. This study shows that how these models are evaluated can significantly impact perceived performance. Clinicians and developers should ensure that evaluation standards are independent and clinically relevant to avoid overestimating AI\u2019s diagnostic abilities and to build trust for real-world use and regulatory approval.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s00784-025-06672-z","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T02:33:50Z","timestamp":1764815630000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Accuracy of deep learning-based AI models for early caries lesion detection: the influence of annotation quality and reference choice"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6727-2516","authenticated-orcid":false,"given":"Ricardo E.","family":"Gonzalez-Valenzuela","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9275-5942","authenticated-orcid":false,"given":"Pascal","family":"Mettes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8794-552X","authenticated-orcid":false,"given":"Bruno G.","family":"Loos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1414-6313","authenticated-orcid":false,"given":"Henk","family":"Marquering","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0718-6763","authenticated-orcid":false,"given":"Erwin","family":"Berkhout","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,4]]},"reference":[{"issue":"3","key":"6672_CR1","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.cden.2021.02.002","volume":"65","author":"AF Dayo","year":"2021","unstructured":"Dayo AF, Wolff MS, Syed AZ, Mupparapu M (2021) Radiology of dental caries. 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An addendum covering the extension to the ACTA-DIRECT v2 dataset was subsequently reviewed and approved under 2023-17752. A001. The storage procedure for extracted teeth within the ACTA Oral Radiology Department does not allow linkage of individual teeth to identifiable patient records; as a result, the material is anonymized. Consequently, patient consent was not required, in accordance with the ACTA IRB\u2019s determination.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}],"article-number":"598"}}