The U-Net Approaches to Evaluation of Dental Bite-Wing Radiographs: An Artificial Intelligence Study

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dc.authoridRozylo-Kalinowska, Ingrid/0000-0001-5162-1382
dc.authoridBAYDAR, OGUZHAN/0000-0002-8353-5347
dc.authorscopusid57222583382
dc.authorscopusid6603955520
dc.authorscopusid57427902600
dc.authorscopusid57226724453
dc.contributor.authorBaydar, Oguzhan
dc.contributor.authorRozylo-Kalinowska, Ingrid
dc.contributor.authorFutyma-Gabka, Karolina
dc.contributor.authorSaglam, Hande
dc.date.accessioned2024-08-25T18:53:04Z
dc.date.available2024-08-25T18:53:04Z
dc.date.issued2023
dc.departmentEge Üniversitesien_US
dc.description.abstractBite-wing radiographs are one of the most used intraoral radiography techniques in dentistry. AI is extremely important in terms of more efficient patient care in the field of dentistry. The aim of this study was to perform a diagnostic evaluation on bite-wing radiographs with an AI model based on CNNs. In this study, 500 bite-wing radiographs in the radiography archive of Eskisehir Osmangazi University, Faculty of Dentistry, Department of Oral and Maxillofacial Radiology were used. The CranioCatch labeling program (CranioCatch, Eskisehir, Turkey) with tooth decays, crowns, pulp, restoration material, and root-filling material for five different diagnoses were made by labeling the segmentation technique. The U-Net architecture was used to develop the AI model. F1 score, sensitivity, and precision results of the study, respectively, caries 0.8818-0.8235-0.9491, crown; 0.9629-0.9285-1, pulp; 0.9631-0.9843-0.9429, with restoration material; and 0.9714-0.9622-0.9807 was obtained as 0.9722-0.9459-1 for the root filling material. This study has shown that an AI model can be used to automatically evaluate bite-wing radiographs and the results are promising. Owing to these automatically prepared charts, physicians in a clinical intense tempo will be able to work more efficiently and quickly.en_US
dc.identifier.doi10.3390/diagnostics13030453
dc.identifier.issn2075-4418
dc.identifier.issue3en_US
dc.identifier.pmid36766557en_US
dc.identifier.scopus2-s2.0-85147822100en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/diagnostics13030453
dc.identifier.urihttps://hdl.handle.net/11454/102967
dc.identifier.volume13en_US
dc.identifier.wosWOS:000929362700001en_US
dc.identifier.wosqualityQ1en_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.snmz20240825_Gen_US
dc.subjectartificial intelligenceen_US
dc.subjectbite-wing radiographyen_US
dc.subjectdeep learningen_US
dc.subjectsegmentationen_US
dc.subjectApproximal Cariesen_US
dc.subjectNeural-Networken_US
dc.subjectPerformanceen_US
dc.subjectBitewingsen_US
dc.subjectDiagnosisen_US
dc.subjectProgramen_US
dc.titleThe U-Net Approaches to Evaluation of Dental Bite-Wing Radiographs: An Artificial Intelligence Studyen_US
dc.typeArticleen_US

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