Development of a YOLOv8-based deep learning model for detecting and segmenting dental restorations and dental applications in panoramic radiographs of mixed dentition

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dc.contributor.authorKuran, Alican
dc.contributor.authorGulsen, Ibrahim Tevfik
dc.contributor.authorKizilay, Fatma Nur
dc.contributor.authorGulsen, Emine
dc.contributor.authorAsar, Mustafa Enes
dc.contributor.authorOzudogru, Semanur
dc.contributor.authorUnal, Turkan
dc.date.accessioned2026-01-24T12:31:21Z
dc.date.available2026-01-24T12:31:21Z
dc.date.issued2025
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractBackground The objective of this study was to develop a deep learning (DL) model for the detection and segmentation of six types of dental restorations and applications in panoramic radiographs of paediatric patients with mixed dentition.Material and methods A total of 2,033 panoramic radiographs were labelled for six different dental restorations. The dataset was divided into three parts: 80% for training, 10% for validation, and 10% for testing. The YOLOv8 model was trained for 500 epochs with a learning rate of 0.01. The success of the model was evaluated using sensitivity, precision and F1 score metrics.Results The YOLOv8 multiclass-DL model achieved high performance, with an overall F1 score of 0.89, supported by a sensitivity of 0.85 and precision of 0.93. Among the evaluated restoration types, dental fillings achieved the highest F1-score of 0.97, followed by stainless steel crowns with 0.94, space maintainers with 0.93, pulpotomies with 0.90, and root canal fillings with 0.84. The lowest performance was observed in the detection of dental brackets, which reached an F1-score of only 0.46.Conclusion YOLOv8-based DL models demonstrate a high level of success in detecting and segmenting dental restorations in panoramic radiographs of patients in the mixed dentition period.
dc.identifier.doi10.1038/s41415-025-9009-4
dc.identifier.issn0007-0610
dc.identifier.issn1476-5373
dc.identifier.pmid41350931
dc.identifier.scopus2-s2.0-105024212143
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1038/s41415-025-9009-4
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5824
dc.identifier.wosWOS:001631214500001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringernature
dc.relation.ispartofBritish Dental Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260121
dc.subjectArtificial-Intelligence
dc.titleDevelopment of a YOLOv8-based deep learning model for detecting and segmenting dental restorations and dental applications in panoramic radiographs of mixed dentition
dc.typeArticle

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