An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs

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dc.authoridakarsu, serdar/0000-0002-7816-1635
dc.authoridAtasoy, Samet/0000-0002-7439-1046
dc.authoridBAYRAKDAR, Ibrahim Sevki/0000-0001-5036-9867
dc.authoridOrhan, Kaan/0000-0001-6768-0176
dc.contributor.authorYasa, Yasin
dc.contributor.authorCelik, Ozer
dc.contributor.authorBayrakdar, Ibrahim Sevki
dc.contributor.authorPekince, Adem
dc.contributor.authorOrhan, Kaan
dc.contributor.authorAkarsu, Serdar
dc.contributor.authorAtasoy, Samet
dc.date.accessioned2024-09-29T16:01:11Z
dc.date.available2024-09-29T16:01:11Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description.abstractObjectives Radiological examination has an important place in dental practice, and it is frequently used in intraoral imaging. The correct numbering of teeth on radiographs is a routine practice that takes time for the dentist. This study aimed to propose an automatic detection system for the numbering of teeth in bitewing images using a faster Region-based Convolutional Neural Networks (R-CNN) method. Methods The study included 1125 bite-wing radiographs of patients who attended the Faculty of Dentistry of Eskisehir Osmangazi University from 2018 to 2019. A faster R-CNN an advanced object identification method was used to identify the teeth. The confusion matrix was used as a metric and to evaluate the success of the model. Results The deep CNN system (CranioCatch, Eskisehir, Turkey) was used to detect and number teeth in bitewing radiographs. Of 715 teeth in 109 bite-wing images, 697 were correctly numbered in the test data set. The F1 score, precision and sensitivity were 0.9515, 0.9293 and 0.9748, respectively. Conclusions A CNN approach for the analysis of bitewing images shows promise for detecting and numbering teeth. This method can save dentists time by automatically preparing dental charts.en_US
dc.identifier.doi10.1080/00016357.2020.1840624
dc.identifier.endpage281en_US
dc.identifier.issn0001-6357
dc.identifier.issn1502-3850
dc.identifier.issue4en_US
dc.identifier.pmid33176533en_US
dc.identifier.scopus2-s2.0-85096129952en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage275en_US
dc.identifier.urihttps://doi.org/10.1080/00016357.2020.1840624
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5579
dc.identifier.volume79en_US
dc.identifier.wosWOS:000588525100001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofActa Odontologica Scandinavicaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectdeep learningen_US
dc.subjecttooth detectionen_US
dc.subjectbite-wing radiographyen_US
dc.titleAn artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographsen_US
dc.typeArticleen_US

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