A Deep Learning Approach to Automatic Tooth Caries Segmentation in Panoramic Radiographs of Children in Primary Dentition, Mixed Dentition, and Permanent Dentition

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dc.authoridBircan, Hasan Basri/0000-0003-2621-3947
dc.authoridBAYRAKDAR, Ibrahim Sevki/0000-0001-5036-9867
dc.authoridCelik, Ozer/0000-0002-4409-3101
dc.authoridOrhan, Kaan/0000-0001-6768-0176
dc.contributor.authorAsci, Esra
dc.contributor.authorKilic, Munevver
dc.contributor.authorCelik, Ozer
dc.contributor.authorCantekin, Kenan
dc.contributor.authorBircan, Hasan Basri
dc.contributor.authorBayrakdar, Ibrahim Sevki
dc.contributor.authorOrhan, Kaan
dc.date.accessioned2025-03-09T10:48:35Z
dc.date.available2025-03-09T10:48:35Z
dc.date.issued2024
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractObjectives: 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.
dc.identifier.doi10.3390/children11060690
dc.identifier.issn2227-9067
dc.identifier.issue6
dc.identifier.pmid38929269
dc.identifier.scopus2-s2.0-85197228440
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/children11060690
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4608
dc.identifier.volume11
dc.identifier.wosWOS:001254717300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofChildren-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250310
dc.subjectcaries
dc.subjectArtificial Intelligence
dc.subjectpanoramic radiography
dc.subjectdeep learning
dc.titleA Deep Learning Approach to Automatic Tooth Caries Segmentation in Panoramic Radiographs of Children in Primary Dentition, Mixed Dentition, and Permanent Dentition
dc.typeArticle

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