Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images

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dc.authoridaltun, oguzhan/0000-0002-5020-8032
dc.authoridBAYRAKDAR, Ibrahim Sevki/0000-0001-5036-9867
dc.authoridEser, Gozde/0000-0003-4170-7929
dc.authoridduman, suayip burak/0000-0003-2552-0187
dc.authoridOrhan, Kaan/0000-0001-6768-0176
dc.authoridZ. Abdelkarim, Ahmed/0000-0001-9525-7527
dc.authorwosidaltun, oguzhan/ABH-4382-2020
dc.authorwosidBAYRAKDAR, Ibrahim Sevki/Y-1232-2019
dc.authorwosidEser, Gozde/ADR-8081-2022
dc.authorwosidduman, suayip burak/ABE-5878-2020
dc.authorwosidÇelik, Önder/HSE-8681-2023
dc.contributor.authorDuman, Suayip Burak
dc.contributor.authorSyed, Ali Z.
dc.contributor.authorOzen, Duygu Celik
dc.contributor.authorBayrakdar, Ibrahim Sevki
dc.contributor.authorSalehi, Hassan S.
dc.contributor.authorAbdelkarim, Ahmed
dc.contributor.authorCelik, Ozer
dc.date.accessioned2024-08-04T20:53:02Z
dc.date.available2024-08-04T20:53:02Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe present study aims to validate the diagnostic performance and evaluate the reliability of an artificial intelligence system based on the convolutional neural network method for the morphological classification of sella turcica in CBCT (cone-beam computed tomography) images. In this retrospective study, sella segmentation and classification models (CranioCatch, Eskisehir, Turkiye) were applied to sagittal slices of CBCT images, using PyTorch supported by U-Net and TensorFlow 1, and we implemented the GoogleNet Inception V3 algorithm. The AI models achieved successful results for sella turcica segmentation of CBCT images based on the deep learning models. The sensitivity, precision, and F-measure values were 1.0, 1.0, and 1.0, respectively, for segmentation of sella turcica in sagittal slices of CBCT images. The sensitivity, precision, accuracy, and F1-score were 1.0, 0.95, 0.98, and 0.84, respectively, for sella-turcica-flattened classification; 0.95, 0.83, 0.92, and 0.88, respectively, for sella-turcica-oval classification; 0.75, 0.94, 0.90, and 0.83, respectively, for sella-turcica-round classification. It is predicted that detecting anatomical landmarks with orthodontic importance, such as the sella point, with artificial intelligence algorithms will save time for orthodontists and facilitate diagnosis.en_US
dc.description.sponsorshipEskisehir Osmangazi University Scientific Research Projects Coordination Unit [202045E06]en_US
dc.description.sponsorshipThis work was supported by the Eskisehir Osmangazi University Scientific Research Projects Coordination Unit under grant number 202045E06.en_US
dc.identifier.doi10.3390/diagnostics12092244
dc.identifier.issn2075-4418
dc.identifier.issue9en_US
dc.identifier.pmid36140645en_US
dc.identifier.scopus2-s2.0-85138490078en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/diagnostics12092244
dc.identifier.urihttps://hdl.handle.net/11616/100903
dc.identifier.volume12en_US
dc.identifier.wosWOS:000856337900001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofDiagnosticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectsella turcicaen_US
dc.subjectartificial intelligenceen_US
dc.subjectCBCTen_US
dc.subjectconvolutional neural networken_US
dc.titleConvolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Imagesen_US
dc.typeArticleen_US

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