A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs

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dc.authorid0000-0002-6429-4197
dc.authorid0000-0002-6842-1528
dc.contributor.authorKaya, Emine
dc.contributor.authorGüneç, Hüseyin Gürkan
dc.contributor.authorCesur Aydın, Kader
dc.contributor.authorÜrkmez, Elif Şeyda
dc.contributor.authorDuranay, Recep
dc.contributor.authorAteş, Hasan Fehmi
dc.date.accessioned2022-10-31T07:59:03Z
dc.date.available2022-10-31T07:59:03Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Diş Hekimliği Fakültesi, Ağız, Diş ve Çene Radyolojisi Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractPurpose: The aim of this study was to assess the performance of a deep learning system for permanent tooth germ detection on pediatric panoramic radiographs.Materials and Methods: In total, 4518 anonymized panoramic radiographs of children between 5 and 13 years of age were collected. YOLOv4, a convolutional neural network (CNN)-based object detection model, was used to automatically detect permanent tooth germs. Panoramic images of children processed in LabelImg were trained and tested in the YOLOv4 algorithm. True-positive, false-positive, and false-negative rates were calculated. A confusion matrix was used to evaluate the performance of the model.Results: The YOLOv4 model, which detected permanent tooth germs on pediatric panoramic radiographs, provided an average precision value of 94.16% and an F1 value of 0.90, indicating a high level of significance. The average YOLOv4 inference time was 90 ms. Conclusion: The detection of permanent tooth germs on pediatric panoramic X-rays using a deep learning-based approach may facilitate the early diagnosis of tooth deficiency or supernumerary teeth and help dental practitioners find more accurate treatment options while saving time and effort.
dc.identifier.citationKaya, E., Güneç, H. G., Cesur Aydın, K., Ürkmez, E. Ş., Duranay, R. ve Ateş, H. F. (2022). A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs. Imaging Science in Dentistry, 52(3), 275-281. https://doi.org/10.5624/isd.20220050
dc.identifier.doi10.5624/isd.20220050
dc.identifier.endpage281
dc.identifier.issn2233-7822
dc.identifier.issn2233-7830
dc.identifier.issue3
dc.identifier.pmid36238699
dc.identifier.scopus2-s2.0-85140032131
dc.identifier.scopusqualityQ2
dc.identifier.startpage275
dc.identifier.urihttps://doi.org/10.5624/isd.20220050
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9894
dc.identifier.volume52
dc.identifier.wos000853675700001en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorCesur Aydın, Kader
dc.institutionauthorAteş, Hasan Fehmi
dc.language.isoen
dc.publisherKorean Acad Oral and Maxillofacial Radiology
dc.relation.ispartofImaging Science in Dentistryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsAttribution-NonCommercial 3.0 Unported*
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/*
dc.subjectPanoramic
dc.subjectPediatric Dentistry
dc.subjectRadiograph
dc.subjectTooth Germ
dc.titleA deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs
dc.typeArticle

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