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.accessioned2024-08-04T20:56:12Z
dc.date.available2024-08-04T20:56:12Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
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.en_US
dc.description.sponsorshipDAS:The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.en_US
dc.identifier.doi10.1186/s12880-024-01338-w
dc.identifier.issn1471-2342
dc.identifier.issue1en_US
dc.identifier.pmid38992601en_US
dc.identifier.scopus2-s2.0-85198068975en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1186/s12880-024-01338-w
dc.identifier.urihttps://hdl.handle.net/11616/102110
dc.identifier.volume24en_US
dc.identifier.wosWOS:001266641400002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherBmcen_US
dc.relation.ispartofBmc Medical Imagingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectDeep learningen_US
dc.subjectTooth enumerationen_US
dc.subjectPanoramic radiographsen_US
dc.subjectPediatric dentistryen_US
dc.titleYOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentitionen_US
dc.typeArticleen_US

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