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.authoridJagtap, Rohan/0000-0002-9115-7235
dc.authoridDUMAN, SUAYIP BURAK/0000-0003-2552-0187
dc.authoridHelvacioglu-Yigit, Dilek/0000-0001-5999-9726
dc.authorwosidBaydar, Oğuzhan/AEW-8550-2022
dc.authorwosidHelvacioglu-Yigit, Dilek/HKN-7458-2023
dc.contributor.authorDuman, Suayip Burak
dc.contributor.authorOzen, Duygu Celik
dc.contributor.authorBayrakdar, Ibrahim Sevki
dc.contributor.authorBaydar, Oguzhan
dc.contributor.authorAbu Alhaija, Elham S.
dc.contributor.authorYigit, Dilek Helvacioglu
dc.contributor.authorCelik, Oezer
dc.date.accessioned2024-08-04T20:54:47Z
dc.date.available2024-08-04T20:54:47Z
dc.date.issued2024
dc.departmentİnönü 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.endpage561en_US
dc.identifier.issn1618-1247
dc.identifier.issn1618-1255
dc.identifier.issue2en_US
dc.identifier.pmid37907818en_US
dc.identifier.scopus2-s2.0-85175301752en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage552en_US
dc.identifier.urihttps://doi.org/10.1007/s10266-023-00864-3
dc.identifier.urihttps://hdl.handle.net/11616/101640
dc.identifier.volume112en_US
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.subjectSecond mesiobuccal canalsen_US
dc.subjectCone beam computed tomographyen_US
dc.subjectDeep learningen_US
dc.subjectYOLOen_US
dc.titleSecond mesiobuccal canal segmentation with YOLOv5 architecture using cone beam computed tomography imagesen_US
dc.typeArticleen_US

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