A novel deep learning-based perspective for tooth numbering and caries detection

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dc.authoridBayraktar, Yusuf/0000-0001-6250-5651
dc.authoridAyhan, Baturalp/0000-0002-7488-895X
dc.contributor.authorAyhan, Baturalp
dc.contributor.authorAyan, Enes
dc.contributor.authorBayraktar, Yusuf
dc.date.accessioned2025-01-21T16:35:12Z
dc.date.available2025-01-21T16:35:12Z
dc.date.issued2024
dc.departmentKırıkkale Üniversitesi
dc.description.abstractObjectivesThe aim of this study was automatically detecting and numbering teeth in digital bitewing radiographs obtained from patients, and evaluating the diagnostic efficiency of decayed teeth in real time, using deep learning algorithms.MethodsThe dataset consisted of 1170 anonymized digital bitewing radiographs randomly obtained from faculty archives. After image evaluation and labeling process, the dataset was split into training and test datasets. This study proposed an end-to-end pipeline architecture consisting of three stages for matching tooth numbers and caries lesions to enhance treatment outcomes and prevent potential issues. Initially, a pre-trained convolutional neural network (CNN) utilized to determine the side of the bitewing images. Then, an improved CNN model YOLOv7 was proposed for tooth numbering and caries detection. In the final stage, our developed algorithm assessed which teeth have caries by comparing the numbered teeth with the detected caries, using the intersection over union value for the matching process.ResultsAccording to test results, the recall, precision, and F1-score values were 0.994, 0.987 and 0.99 for teeth detection, 0.974, 0.985 and 0.979 for teeth numbering, and 0.833, 0.866 and 0.822 for caries detection, respectively. For teeth numbering and caries detection matching performance; the accuracy, recall, specificity, precision and F1-Score values were 0.934, 0.834, 0.961, 0.851 and 0.842, respectively.ConclusionsThe proposed model exhibited good achievement, highlighting the potential use of CNNs for tooth detection, numbering, and caries detection, concurrently.Clinical significanceCNNs can provide valuable support to clinicians by automating the detection and numbering of teeth, as well as the detection of caries on bitewing radiographs. By enhancing overall performance, these algorithms have the capacity to efficiently save time and play a significant role in the assessment process.
dc.description.sponsorshipKirikkale University
dc.description.sponsorshipThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
dc.identifier.doi10.1007/s00784-024-05566-w
dc.identifier.issn1432-6981
dc.identifier.issn1436-3771
dc.identifier.issue3
dc.identifier.pmid38411726
dc.identifier.scopus2-s2.0-85186209508
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s00784-024-05566-w
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24094
dc.identifier.volume28
dc.identifier.wosWOS:001172677100001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofClinical Oral Investigations
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectArtificial intelligence; Digital bitewing radiography; Deep learning; Detection; Numbering; Dental caries
dc.titleA novel deep learning-based perspective for tooth numbering and caries detection
dc.typeArticle

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