Automatic Feature Segmentation in Dental Periapical Radiographs

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dc.authoridduman, suayip burak/0000-0003-2552-0187
dc.authoridBAYRAKDAR, Ibrahim Sevki/0000-0001-5036-9867
dc.authoridJagtap, Rohan/0000-0002-9115-7235
dc.authoridRozylo-Kalinowska, Ingrid/0000-0001-5162-1382
dc.authoridOrhan, Kaan/0000-0001-6768-0176
dc.authoridOksuzoglu, Hasan/0000-0002-8300-6963
dc.authorwosidduman, suayip burak/ABE-5878-2020
dc.authorwosidBAYRAKDAR, Ibrahim Sevki/Y-1232-2019
dc.authorwosidJagtap, Rohan/AAR-4407-2021
dc.contributor.authorAri, Tugba
dc.contributor.authorSaglam, Hande
dc.contributor.authorOksuzoglu, Hasan
dc.contributor.authorKazan, Orhan
dc.contributor.authorBayrakdar, Ibrahim Sevki
dc.contributor.authorDuman, Suayip Burak
dc.contributor.authorCelik, Ozer
dc.date.accessioned2024-08-04T20:53:17Z
dc.date.available2024-08-04T20:53:17Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractWhile a large number of archived digital images make it easy for radiology to provide data for Artificial Intelligence (AI) evaluation; AI algorithms are more and more applied in detecting diseases. The aim of the study is to perform a diagnostic evaluation on periapical radiographs with an AI model based on Convoluted Neural Networks (CNNs). The dataset includes 1169 adult periapical radiographs, which were labelled in CranioCatch annotation software. Deep learning was performed using the U-Net model implemented with the PyTorch library. The AI models based on deep learning models improved the success rate of carious lesion, crown, dental pulp, dental filling, periapical lesion, and root canal filling segmentation in periapical images. Sensitivity, precision and F1 scores for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown were 1, 1, and 1, respectively; sensitivity, precision and F1 score for dental pulp, were 0.97, 0.87 and 0.92, respectively; sensitivity, precision and F1 score for filling were 0.95, 0.95, and 0.95, respectively; sensitivity, precision and F1 score for the periapical lesion were 0.92, 0.85, and 0.88, respectively; sensitivity, precision and F1 score for root canal filling, were found to be 1, 0.96, and 0.98, respectively. The success of AI algorithms in evaluating periapical radiographs is encouraging and promising for their use in routine clinical processes as a clinical decision support system.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.doi10.3390/diagnostics12123081
dc.identifier.issn2075-4418
dc.identifier.issue12en_US
dc.identifier.pmid36553088en_US
dc.identifier.scopus2-s2.0-85144874795en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/diagnostics12123081
dc.identifier.urihttps://hdl.handle.net/11616/101081
dc.identifier.volume12en_US
dc.identifier.wosWOS:000900619600001en_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.subjectartificial intelligenceen_US
dc.subjectperiapical radiographsen_US
dc.subjectdeep learningen_US
dc.subjectoral diseasesen_US
dc.subjectoral findingsen_US
dc.titleAutomatic Feature Segmentation in Dental Periapical Radiographsen_US
dc.typeArticleen_US

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