Automated Mesiodens Detection with Deep-Learning-Based System Using Cone-Beam Computed Tomography Images

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dc.authoridZ. Abdelkarim, Ahmed/0000-0001-9525-7527
dc.authoridDUMAN, SUAYIP BURAK/0000-0003-2552-0187
dc.authoridCelik, Ozer/0000-0002-4409-3101
dc.authoridBAYRAKDAR, Ibrahim Sevki/0000-0001-5036-9867
dc.authoridCelik Ozen, Duygu/0000-0001-7274-3987
dc.authoridOrhan, Kaan/0000-0001-6768-0176
dc.authorwosidBAYRAKDAR, Ibrahim Sevki/B-2411-2015
dc.contributor.authorSyed, Ali Zakir
dc.contributor.authorOzen, Duygu Celik
dc.contributor.authorAbdelkarim, Ahmed Z.
dc.contributor.authorDuman, Suayip Burak
dc.contributor.authorBayrakdar, Ibrahim Sevki
dc.contributor.authorDuman, Sacide
dc.contributor.authorCelik, Ozer
dc.date.accessioned2024-08-04T20:54:49Z
dc.date.available2024-08-04T20:54:49Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe detection of mesiodens supernumerary teeth is crucial for appropriate diagnosis and treatment. The study aimed to develop a convolutional neural network (CNN)-based model to automatically detect mesiodens in cone-beam computed tomography images. A datatest of anonymized 851 axial slices of 106 patients' cone-beam images was used to process the artificial intelligence system for the detection and segmentation of mesiodens. The CNN model achieved high performance in mesiodens segmentation with sensitivity, precision, and F1 scores of 1, 0.9072, and 0.9513, respectively. The area under the curve (AUC) was 0.9147, indicating the model's robustness. The proposed model showed promising potential for the automated detection of mesiodens, providing valuable assistance to dentists in accurate diagnosis.en_US
dc.description.sponsorshipThis work has been supported by Eskisehir Osmangazi University Scientific Research Projects Coordination Unit under Grant no. 202045E06. [202045E06]; Eskisehir Osmangazi University Scientific Research Projects Coordination Uniten_US
dc.description.sponsorshipThis work has been supported by Eskisehir Osmangazi University Scientific Research Projects Coordination Unit under Grant no. 202045E06.en_US
dc.identifier.doi10.1155/2023/4415970
dc.identifier.issn0884-8173
dc.identifier.issn1098-111X
dc.identifier.scopus2-s2.0-85176281741en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1155/2023/4415970
dc.identifier.urihttps://hdl.handle.net/11616/101664
dc.identifier.volume2023en_US
dc.identifier.wosWOS:001094644700001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWiley-Hindawien_US
dc.relation.ispartofInternational Journal of Intelligent Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectClassificationen_US
dc.subjectTeethen_US
dc.titleAutomated Mesiodens Detection with Deep-Learning-Based System Using Cone-Beam Computed Tomography Imagesen_US
dc.typeArticleen_US

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