Imaging Science in Dentistry

Korean Academy of Oral and Maxillofacial Radiology (대한영상치의학회)
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A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs

  • Kaya, Emine (Department of Pediatric Dentistry, Faculty of Dentistry, Istanbul Okan University) ;
  • Gunec, Huseyin Gurkan (Department of Endodontics, Faculty of Dentistry, Atlas University) ;
  • Aydin, Kader Cesur (Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Istanbul Medipol University) ;
  • Urkmez, Elif Seyda (Basaksehir Inci ADSM) ;
  • Duranay, Recep (Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Atlas University) ;
  • Ates, Hasan Fehmi (Department of Computer Engineering, School of Engineering and Natural Sciences, Istanbul Medipol University)
  • Received : 2022.03.10
  • Accepted : 2022.06.01
  • Published : 2022.09.30
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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

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References

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