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http://dx.doi.org/10.5624/isd.20220050

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)
Publication Information
Imaging Science in Dentistry / v.52, no.3, 2022 , pp. 275-281 More about this Journal
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
Keywords
Tooth Germ; Radiograph, Panoramic; Pediatric Dentistry;
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