Automatic deep learning detection of overhanging restorations in bitewing radiographs

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dc.authoridOrhan, Kaan/0000-0001-6768-0176
dc.authoridMagat, Guldane/0000-0003-4418-174X
dc.contributor.authorMagat, Guldane
dc.contributor.authorAltindag, Ali
dc.contributor.authorHatipoglu, Fatma Pertek
dc.contributor.authorHatipoglu, Omer
dc.contributor.authorBayrakdar, Ibrahim Sevki
dc.contributor.authorCelik, Ozer
dc.contributor.authorOrhan, Kaan
dc.date.accessioned2024-11-07T13:35:13Z
dc.date.available2024-11-07T13:35:13Z
dc.date.issued2024
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractObjectives This study aimed to assess the effectiveness of deep convolutional neural network (CNN) algorithms for the detecting and segmentation of overhanging dental restorations in bitewing radiographs.Methods A total of 1160 anonymized bitewing radiographs were used to progress the artificial intelligence (AI) system for the detection and segmentation of overhanging restorations. The data were then divided into three groups: 80% for training (930 images, 2399 labels), 10% for validation (115 images, 273 labels), and 10% for testing (115 images, 306 labels). A CNN model known as You Only Look Once (YOLOv5) was trained to detect overhanging restorations in bitewing radiographs. After utilizing the remaining 115 radiographs to evaluate the efficacy of the proposed CNN model, the accuracy, sensitivity, precision, F1 score, and area under the receiver operating characteristic curve (AUC) were computed.Results The model demonstrated a precision of 90.9%, a sensitivity of 85.3%, and an F1 score of 88.0%. Furthermore, the model achieved an AUC of 0.859 on the receiver operating characteristic (ROC) curve. The mean average precision (mAP) at an intersection over a union (IoU) threshold of 0.5 was notably high at 0.87.Conclusions The findings suggest that deep CNN algorithms are highly effective in the detection and diagnosis of overhanging dental restorations in bitewing radiographs. The high levels of precision, sensitivity, and F1 score, along with the significant AUC and mAP values, underscore the potential of these advanced deep learning techniques in revolutionizing dental diagnostic procedures.
dc.identifier.doi10.1093/dmfr/twae036
dc.identifier.issn0250-832X
dc.identifier.issn1476-542X
dc.identifier.issue7
dc.identifier.pmid39024043
dc.identifier.scopus2-s2.0-85205401651
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1093/dmfr/twae036
dc.identifier.urihttps://hdl.handle.net/11480/16380
dc.identifier.volume53
dc.identifier.wosWOS:001282383800001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherOxford Univ Press
dc.relation.ispartofDentomaxillofacial Radiology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectartificial intelligence
dc.subjectbitewing
dc.subjectdeep learning
dc.subjectoverhanging restoration
dc.titleAutomatic deep learning detection of overhanging restorations in bitewing radiographs
dc.typeArticle

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