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

Kaya E., Gunec H. G., Aydin K., Urkmez E. S., Duranay R., Ates H. F.

Imaging Science in Dentistry, vol.52, no.3, pp.275-281, 2022 (ESCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 52 Issue: 3
  • Publication Date: 2022
  • Doi Number: 10.5624/isd.20220050
  • Journal Name: Imaging Science in Dentistry
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, Directory of Open Access Journals
  • Page Numbers: pp.275-281
  • Keywords: Tooth Germ, Radiograph, Panoramic, Pediatric Dentistry
  • Istanbul Medipol University Affiliated: Yes


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