A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs
- Author:
Emine KAYA
1
;
Huseyin Gurkan GUNEC
;
Kader Cesur AYDIN
;
Elif Seyda URKMEZ
;
Recep DURANAY
;
Hasan Fehmi ATES
Author Information
- Publication Type:Original Articles
- From:Imaging Science in Dentistry 2022;52(3):275-281
- CountryRepublic of Korea
- Language:English
-
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.
