A deep learning approach to dental restoration classification from bitewing and periapical radiographs

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dc.authoridOZSARIYILDIZ, Saban Suat/0000-0002-3467-0630
dc.contributor.authorKarataş, Özcan
dc.contributor.authorCakir, Nazire Nurdan
dc.contributor.authorOzsariyildiz, Saban Suat
dc.contributor.authorKis, Hatice Cansu
dc.contributor.authorDemirbuğa, Sezer
dc.contributor.authorGurgan, Cem Abdulkadir
dc.date.accessioned2025-02-24T17:18:57Z
dc.date.available2025-02-24T17:18:57Z
dc.date.issued2021
dc.departmentFakülteler, Diş Hekimliği Fakültesi, Restoratif Diş Tedavisi Ana Bilim Dalı
dc.description.abstractObjective: The aim of this study was to examine the success of deep learning-based convolutional neural networks (CNN) in the detection and differentiation of amalgam, composite resin, and metal-ceramic restorations from bitewing and periapical radiographs. Method and materials: Five hundred and fifty bitewing and periapical radiographs were used. Eighty percent of the images were used for training, and 20% were left for testing. Twenty percent of the images allocated for training were then used for validation during learning. The image classification model was based on the application of CNN. The model used Resnet34 architecture, which is pre-trained on the ImageNet dataset. Average sensitivity, receiver operating characteristic (ROC) curve, and area under the curve (AUC) were calculated for performance evaluation of the model Results: The model training loss was 0.13, and the validation loss was 0.63. The independent test group result was 0.67. Amalgam AUC was 0.95, composite AUC was 0.95, and metal-ceramic AUC was 1.00. The average AUC was 0.97. The false positive rate in the validation set was 18, the false negative rate was 18, the true positive rate was 60, and the true negative rate was 138. The true positive rate was 0.82 for amalgam, 0.75 for composite, and 0.73 for metal-ceramic. Conclusion: Deep leaming-based CNNs from periapical and bitewing radiographs appear to be a promising technique for the detection and differentiation of restorations.
dc.identifier.doi10.3290/j.qi.b1244461
dc.identifier.endpage574
dc.identifier.issn0033-6572
dc.identifier.issn1936-7163
dc.identifier.issue7
dc.identifier.pmid33880914
dc.identifier.scopus2-s2.0-85108025700
dc.identifier.scopusqualityQ2
dc.identifier.startpage568
dc.identifier.urihttps://doi.org/10.3290/j.qi.b1244461
dc.identifier.urihttps://hdl.handle.net/20.500.14440/930
dc.identifier.volume52
dc.identifier.wosWOS:000726514300002
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherQuintessence Publishing Co Inc
dc.relation.ispartofQuintessence International
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250201
dc.subjectartificial intelligence
dc.subjectdental restorations
dc.subjectdigital radiology
dc.titleA deep learning approach to dental restoration classification from bitewing and periapical radiographs
dc.typeArticle

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