{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T13:53:54Z","timestamp":1791554034288,"version":"4.3.4"},"reference-count":41,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T00:00:00Z","timestamp":1717545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Children"],"abstract":"<jats:p>Objectives: The purpose of this study was to evaluate the effectiveness of dental caries segmentation on the panoramic radiographs taken from children in primary dentition, mixed dentition, and permanent dentition with Artificial Intelligence (AI) models developed using the deep learning method. Methods: This study used 6075 panoramic radiographs taken from children aged between 4 and 14 to develop the AI model. The radiographs included in the study were divided into three groups: primary dentition (n: 1857), mixed dentition (n: 1406), and permanent dentition (n: 2812). The U-Net model implemented with PyTorch library was used for the segmentation of caries lesions. A confusion matrix was used to evaluate model performance. Results: In the primary dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8525, 0.9128, and 0.8816, respectively. In the mixed dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.7377, 0.9192, and 0.8185, respectively. In the permanent dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8271, 0.9125, and 0.8677, respectively. In the total group including primary, mixed, and permanent dentition, the sensitivity, precision, and F1 scores calculated using the confusion matrix were 0.8269, 0.9123, and 0.8675, respectively. Conclusions: Deep learning-based AI models are promising tools for the detection and diagnosis of caries in panoramic radiographs taken from children with different dentition.<\/jats:p>","DOI":"10.3390\/children11060690","type":"journal-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T11:50:09Z","timestamp":1717588209000},"page":"690","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Deep Learning Approach to Automatic Tooth Caries Segmentation in Panoramic Radiographs of Children in Primary Dentition, Mixed Dentition, and Permanent Dentition"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4647-6701","authenticated-orcid":false,"given":"Esra","family":"Asci","sequence":"first","affiliation":[{"name":"Department of Pediatric Dentistry, Faculty of Dentistry, Ataturk University, Erzurum 25240, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3470-0430","authenticated-orcid":false,"given":"Munevver","family":"Kilic","sequence":"additional","affiliation":[{"name":"Department of Pediatric Dentistry, Faculty of Dentistry, Beykent University, \u0130stanbul 34398, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4409-3101","authenticated-orcid":false,"given":"Ozer","family":"Celik","sequence":"additional","affiliation":[{"name":"Department of Mathematics Computer, Faculty of Science, Eskisehir Osmangazi University, Eskisehir 26040, Turkey"},{"name":"Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir Osmangazi University, Eskisehir 26040, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenan","family":"Cantekin","sequence":"additional","affiliation":[{"name":"Department of Pediatric Dentistry, Faculty of Dentistry, Sakarya University, Sakarya 54050, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2621-3947","authenticated-orcid":false,"given":"Hasan Basri","family":"Bircan","sequence":"additional","affiliation":[{"name":"Department of Pediatric Dentistry, Faculty of Dentistry, Ataturk University, Erzurum 25240, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5036-9867","authenticated-orcid":false,"given":"\u0130brahim Sevki","family":"Bayrakdar","sequence":"additional","affiliation":[{"name":"Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir Osmangazi University, Eskisehir 26040, Turkey"},{"name":"Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir 26040, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6768-0176","authenticated-orcid":false,"given":"Kaan","family":"Orhan","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06620, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"887","DOI":"10.14219\/jada.archive.2000.0307","article-title":"The science and practice of caries prevention","volume":"131","author":"Featherstone","year":"2000","journal-title":"J. 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