Second mesiobuccal canal segmentation with YOLOv5 architecture using cone beam computed tomography images

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dc.authoridBaydar, Oğuzhan/0000-0002-8353-5347
dc.authorscopusid56494486200
dc.authorscopusid57795443300
dc.authorscopusid55751747900
dc.authorscopusid57222583382
dc.authorscopusid57479569300
dc.authorscopusid54966854300
dc.authorscopusid57215643433
dc.authorwosidBaydar, Oğuzhan/AEW-8550-2022
dc.contributor.authorDuman, Şuayip Burak
dc.contributor.authorOzen, Duygu Celik
dc.contributor.authorBayrakdar, İbrahim Şevki
dc.contributor.authorBaydar, Oguzhan
dc.contributor.authorAbu Alhaija, Elham S.
dc.contributor.authorHelvacıoğlu Yiğit, Dilek
dc.contributor.authorÇelik, Özer
dc.date.accessioned2024-08-25T18:47:30Z
dc.date.available2024-08-25T18:47:30Z
dc.date.issued2023
dc.departmentEge Üniversitesien_US
dc.description.abstractThe objective of this study is to use a deep-learning model based on CNN architecture to detect the second mesiobuccal (MB2) canals, which are seen as a variation in maxillary molars root canals. In the current study, 922 axial sections from 153 patients' cone beam computed tomography (CBCT) images were used. The segmentation method was employed to identify the MB2 canals in maxillary molars that had not previously had endodontic treatment. Labeled images were divided into training (80%), validation (10%) and testing (10%) groups. The artificial intelligence (AI) model was trained using the You Only Look Once v5 (YOLOv5x) architecture with 500 epochs and a learning rate of 0.01. Confusion matrix and receiver-operating characteristic (ROC) analysis were used in the statistical evaluation of the results. The sensitivity of the MB2 canal segmentation model was 0.92, the precision was 0.83, and the F1 score value was 0.87. The area under the curve (AUC) in the ROC graph of the model was 0.84. The mAP value at 0.5 inter-over union (IoU) was found as 0.88. The deep-learning algorithm used showed a high success in the detection of the MB2 canal. The success of the endodontic treatment can be increased and clinicians' time can be preserved using the newly created artificial intelligence-based models to identify variations in root canal anatomy before the treatment.en_US
dc.description.sponsorshipEskisehir Osmangazi University Scientific Research Projects Coordination Unit [202045E06]en_US
dc.description.sponsorshipThis work has been supported by Eskisehir Osmangazi University Scientific Research Projects Coordination Unit under grant number 202045E06.en_US
dc.identifier.doi10.1007/s10266-023-00864-3
dc.identifier.issn1618-1247
dc.identifier.issn1618-1255
dc.identifier.pmid37907818en_US
dc.identifier.scopus2-s2.0-85175301752en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s10266-023-00864-3
dc.identifier.urihttps://hdl.handle.net/11454/101928
dc.identifier.wosWOS:001091308600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofOdontologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240825_Gen_US
dc.subjectSecond mesiobuccal canalsen_US
dc.subjectCone beam computed tomographyen_US
dc.subjectDeep learningen_US
dc.subjectYOLOen_US
dc.subjectMaxillary 1st Molaren_US
dc.subjectRooten_US
dc.subjectMorphologyen_US
dc.subjectCbcten_US
dc.subjectIdentifyen_US
dc.titleSecond mesiobuccal canal segmentation with YOLOv5 architecture using cone beam computed tomography imagesen_US
dc.typeArticleen_US

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