dc.contributor.author | Kaya, Emine | |
dc.contributor.author | Güneç, Hüseyin Gürkan | |
dc.contributor.author | Cesur Aydın, Kader | |
dc.contributor.author | Ürkmez, Elif Şeyda | |
dc.contributor.author | Duranay, Recep | |
dc.contributor.author | Ateş, Hasan Fehmi | |
dc.date.accessioned | 2022-10-31T07:59:03Z | |
dc.date.available | 2022-10-31T07:59:03Z | |
dc.date.issued | 2022 | en_US |
dc.identifier.citation | Kaya, 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 | en_US |
dc.identifier.issn | 2233-7822 | |
dc.identifier.issn | 2233-7830 | |
dc.identifier.uri | https://doi.org/10.5624/isd.20220050 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12511/9894 | |
dc.description.abstract | Purpose: 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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Korean Acad Oral and Maxillofacial Radiology | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.rights | Attribution-NonCommercial 3.0 Unported | * |
dc.rights.uri | https://creativecommons.org/licenses/by-nc/3.0/ | * |
dc.subject | Panoramic | en_US |
dc.subject | Pediatric Dentistry | en_US |
dc.subject | Radiograph | en_US |
dc.subject | Tooth Germ | en_US |
dc.title | A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs | en_US |
dc.type | article | en_US |
dc.relation.ispartof | Imaging Science in Dentistry | en_US |
dc.department | İstanbul Medipol Üniversitesi, Diş Hekimliği Fakültesi, Ağız, Diş ve Çene Radyolojisi Ana Bilim Dalı | en_US |
dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.authorid | 0000-0002-6429-4197 | en_US |
dc.authorid | 0000-0002-6842-1528 | en_US |
dc.identifier.volume | 52 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.startpage | 275 | en_US |
dc.identifier.endpage | 281 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.5624/isd.20220050 | en_US |
dc.institutionauthor | Cesur Aydın, Kader | |
dc.institutionauthor | Ateş, Hasan Fehmi | |
dc.identifier.wos | 000853675700001 | en_US |
dc.identifier.scopus | 2-s2.0-85140032131 | en_US |
dc.identifier.pmid | 36238699 | en_US |
dc.identifier.scopusquality | Q2 | en_US |