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AuthorDuman, Şuayip Burak
AuthorÇelik Özen, Duygu
AuthorBayrakdar, Ibrahim Şevki
AuthorBaydar, Oğuzhan
AuthorAlhaija, Elham S.Abu
AuthorHelvacioğlu Yiğit, Dilek
AuthorÇelik, Özer
AuthorJagtap, Rohan
AuthorPileggi, Roberta
AuthorOrhan, Kaan
Available date2023-11-07T04:45:57Z
Publication Date2023-01-01
Publication NameOdontology
Identifierhttp://dx.doi.org/10.1007/s10266-023-00864-3
CitationDuman, Ş.B., Çelik Özen, D., Bayrakdar, I.Ş. et al. Second mesiobuccal canal segmentation with YOLOv5 architecture using cone beam computed tomography images. Odontology (2023). https://doi.org/10.1007/s10266-023-00864-3
ISSN16181247
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85175301752&origin=inward
URIhttp://hdl.handle.net/10576/49061
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.
SponsorThis work has been supported by Eskisehir Osmangazi University Scientific Research Projects Coordination Unit under grant number 202045E06.
Languageen
PublisherSpringer
SubjectCone beam computed tomography
Deep learning
Second mesiobuccal canals
YOLO
TitleSecond mesiobuccal canal segmentation with YOLOv5 architecture using cone beam computed tomography images
TypeArticle
ESSN1618-1255
dc.accessType Full Text


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