Kaya, EmineGüneç, Hüseyin GürkanCesur Aydın, KaderÜrkmez, Elif ŞeydaDuranay, RecepAteş, Hasan Fehmi2022-10-312022-10-312022Kaya, 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.202200502233-78222233-7830https://doi.org/10.5624/isd.20220050https://hdl.handle.net/20.500.12511/9894Purpose: 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.enAttribution-NonCommercial 3.0 Unportedinfo:eu-repo/semantics/openAccessPanoramicPediatric DentistryRadiographTooth GermArticle52327528110.5624/isd.20220050N/A0008536757000012-s2.0-8514003213136238699Q2