YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition

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dc.contributor.authorBeser, Busra
dc.contributor.authorReis, Tugba
dc.contributor.authorBerber, Merve Nur
dc.contributor.authorTopaloglu, Edanur
dc.contributor.authorGungor, Esra
dc.contributor.authorKilic, Munevver Coruh
dc.contributor.authorDuman, Sacide
dc.date.accessioned2025-03-09T10:48:45Z
dc.date.available2025-03-09T10:48:45Z
dc.date.issued2024
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractObjectivesIn the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs.MethodsA total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed.ResultsThe sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation.ConclusionsYOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition.
dc.identifier.doi10.1186/s12880-024-01338-w
dc.identifier.issn1471-2342
dc.identifier.issue1
dc.identifier.pmid38992601
dc.identifier.scopus2-s2.0-85198068975
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1186/s12880-024-01338-w
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4656
dc.identifier.volume24
dc.identifier.wosWOS:001266641400002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherBMC
dc.relation.ispartofBmc Medical Imaging
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250310
dc.subjectArtificial intelligence
dc.subjectDeep learning
dc.subjectTooth enumeration
dc.subjectPanoramic radiographs
dc.subjectPediatric dentistry
dc.titleYOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition
dc.typeArticle

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