Detecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithm

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dc.authoridJagtap, Rohan/0000-0002-9115-7235
dc.authoridBAYRAKDAR, Ibrahim Sevki/0000-0001-5036-9867
dc.authoridEser, Gozde/0000-0003-4170-7929
dc.authoridduman, sacide/0000-0001-6884-9674
dc.authoridOrhan, Kaan/0000-0001-6768-0176
dc.authoridCosta, Andre Luiz F/0000-0003-4856-5417
dc.authoridCelik, Ozer/0000-0002-4409-3101
dc.authorwosidJagtap, Rohan/AAR-4407-2021
dc.authorwosidBAYRAKDAR, Ibrahim Sevki/Y-1232-2019
dc.authorwosidEser, Gozde/ADR-8081-2022
dc.authorwosidduman, sacide/ABG-8415-2020
dc.authorwosidOrhan, Kaan/I-4026-2019
dc.authorwosidCosta, Andre Luiz F/C-1964-2012
dc.contributor.authorDuman, Sacide
dc.contributor.authorYilmaz, Emir Faruk
dc.contributor.authorEser, Gozde
dc.contributor.authorCelik, Ozer
dc.contributor.authorBayrakdar, Ibrahim Sevki
dc.contributor.authorBilgir, Elif
dc.contributor.authorFerreira Costa, Andre Luiz
dc.date.accessioned2024-08-04T20:51:58Z
dc.date.available2024-08-04T20:51:58Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractObjectives Artificial intelligence (AI) techniques like convolutional neural network (CNN) are a promising breakthrough that can help clinicians analyze medical imaging, diagnose taurodontism, and make therapeutic decisions. The purpose of the study is to develop and evaluate the function of CNN-based AI model to diagnose teeth with taurodontism in panoramic radiography. Methods 434 anonymized, mixed-sized panoramic radiography images over the age of 13 years were used to develop automatic taurodont tooth segmentation models using a Pytorch implemented U-Net model. Datasets were split into train, validation, and test groups of both normal and masked images. The data augmentation method was applied to images of trainings and validation groups with vertical flip images, horizontal flip images, and both flip images. The Confusion Matrix was used to determine the model performance. Results Among the 43 test group images with 126 labels, there were 109 true positives, 29 false positives, and 17 false negatives. The sensitivity, precision, and F1-score values of taurodont tooth segmentation were 0.8650, 0.7898, and 0.8257, respectively. Conclusions CNN's ability to identify taurodontism produced almost identical results to the labeled training data, and the CNN system achieved close to the expert level results in its ability to detect the taurodontism of teeth.en_US
dc.description.sponsorshipEskisehir University Scientific Research Projects Coordination Unit [202045E06]en_US
dc.description.sponsorshipThis work has been supported by Eskisehir University Scientific Research Projects Coordination Unit under Grant number 202045E06.en_US
dc.identifier.doi10.1007/s11282-022-00622-1
dc.identifier.endpage214en_US
dc.identifier.issn0911-6028
dc.identifier.issn1613-9674
dc.identifier.issue1en_US
dc.identifier.pmid35612677en_US
dc.identifier.scopus2-s2.0-85130756098en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage207en_US
dc.identifier.urihttps://doi.org/10.1007/s11282-022-00622-1
dc.identifier.urihttps://hdl.handle.net/11616/100673
dc.identifier.volume39en_US
dc.identifier.wosWOS:000801839700001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofOral Radiologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTaurodontismen_US
dc.subjectArtificial intelligenceen_US
dc.subjectDeep learningen_US
dc.subjectPanoramic radiographsen_US
dc.subjectDentistryen_US
dc.titleDetecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithmen_US
dc.typeArticleen_US

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