Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/58074
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dc.contributor.authorAkgül, N.-
dc.contributor.authorYilmaz, C.-
dc.contributor.authorBilgir, E.-
dc.contributor.authorÇelik, Ö.-
dc.contributor.authorBaydar, O.-
dc.contributor.authorBayrakdar, İŞ.-
dc.date.accessioned2024-10-20T16:20:52Z-
dc.date.available2024-10-20T16:20:52Z-
dc.date.issued2024-
dc.identifier.issn1807-3107-
dc.identifier.urihttps://doi.org/10.1590/1807-3107bor-2024.vol38.0098-
dc.identifier.urihttps://hdl.handle.net/11499/58074-
dc.description.abstractDental fillings, frequently used in dentistry to address various dental tissue issues, may pose problems when not aligned with the anatomical contours and physiology of dental and periodontal tissues. Our study aims to detect the prevalence and distribution of normal and overhanging filling restorations using a deep CNN architecture trained through supervised learning, on panoramic radiography images. A total of 10480 fillings and 2491 overhanging fillings were labeled using CranioCatch software from 2473 and 1850 images, respectively. After the data obtaining phase, validation (80%), training 10%), and test-groups (10%) were formed from images for both labelling. The YOLOv5x architecture was used to develop the AI model. The model's performance was assessed through a confusion matrix and sensitivity, precision, and F1 score values of the model were calculated. For filling, sensitivity is 0.95, precision is 0.97, and F1 score is 0.96; for overhanging were determined to be 0.86, 0.89, and 0.87, respectively. The results demonstrate the capacity of the YOLOv5 algorithm to segment dental radiographs efficiently and accurately and demonstrate proficiency in detecting and distinguishing between normal and overhanging filling restorations.en_US
dc.language.isoenen_US
dc.relation.ispartofBrazilian oral researchen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAlgorithmsen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectDental Restoration, Permanenten_US
dc.subjectHumansen_US
dc.subjectRadiography, Panoramicen_US
dc.subjectReference Valuesen_US
dc.subjectReproducibility of Resultsen_US
dc.subjectalgorithmen_US
dc.subjectartificial intelligenceen_US
dc.subjectdental restorationen_US
dc.subjecthumanen_US
dc.subjectpanoramic radiographyen_US
dc.subjectproceduresen_US
dc.subjectreference valueen_US
dc.subjectreproducibilityen_US
dc.titleA YOLO-V5 approach for the evaluation of normal fillings and overhanging fillings: an artificial intelligence studyen_US
dc.typeArticleen_US
dc.identifier.volume38en_US
dc.identifier.startpagee098en_US
dc.departmentPamukkale Universityen_US
dc.identifier.doi10.1590/1807-3107bor-2024.vol38.0098-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorscopusid6506373608-
dc.authorscopusid57533763200-
dc.authorscopusid57194858125-
dc.authorscopusid57215643433-
dc.authorscopusid57222583382-
dc.authorscopusid55751747900-
dc.identifier.pmid39356905en_US
dc.identifier.scopus2-s2.0-85205528079en_US
dc.identifier.wosWOS:001332184600001en_US
dc.institutionauthor-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.languageiso639-1en-
item.openairetypeArticle-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept06.01. Clinical Sciences-
Appears in Collections:Diş Hekimliği Fakültesi Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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