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The same dataset was processed by an automatic object detection deep learning method. All annotators were compared in terms of the number of errors and intersection over union (IoU) using pairwise comparisons, with respect to the consensus standard, and with respect to the annotator of the training dataset of the automatic method.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The number of lesions marked by experts in 100 images varied between 241 and 425. Pairwise comparisons showed that the automatic method outperformed all dentists except the original annotator in the mean number of errors, while being among the best in terms of IoU. With respect to a consensus standard, the performance of the automatic method was best in terms of the number of errors and slightly below average in terms of IoU. Compared with the original annotator, the automatic method had the highest IoU and only one expert made fewer errors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The automatic method consistently outperformed novices and performed as well as highly experienced dentists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical significance<\/jats:title>\n                    <jats:p>The consensus in caries detection between experts is low. An automatic method based on deep learning can improve both the accuracy and repeatability of caries detection, providing a\u00a0useful second opinion even for very experienced dentists.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00784-024-05528-2","type":"journal-article","created":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T04:02:18Z","timestamp":1707105738000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Automatic caries detection in bitewing radiographs\u2014Part II: experimental comparison"],"prefix":"10.1007","volume":"28","author":[{"given":"Anton\u00edn","family":"Tich\u00fd","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luk\u00e1\u0161","family":"Kunt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Val\u00e9ria","family":"Nagyov\u00e1","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Kybic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,5]]},"reference":[{"issue":"5","key":"5528_CR1","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1177\/0022034515573272","volume":"94","author":"NJ Kassebaum","year":"2015","unstructured":"Kassebaum NJ, Bernab\u00e9 E, Dahiya M, Bhandari B, Murray CJL, Marcenes W (2015) Global burden of untreated caries: a systematic review and metaregression. 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The patients signed a written informed consent, agreeing with the use of their data in anonymized form for research purposes.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"133"}}