Emine Kaya, Assistant Professor, Department of Pediatric Dentistry, Faculty of Dentistry, Istanbul Okan University, Istanbul, Turkey.
Huseyin Gurkan Gunec, Assistant Professor, Department of Endodontics, Faculty of Dentistry, Atlas University, Istanbul, Turkey.
J Clin Pediatr Dent. 2022 Jul 1;46(4):293-298. doi: 10.22514/1053-4625-46.4.6.
In this paper, we aimed to evaluate the performance of a deep learning system for automated tooth detection and numbering on pediatric panoramic radiographs.
YOLO V4, a CNN (Convolutional Neural Networks) based object detection model was used for automated tooth detection and numbering. 4545 pediatric panoramic X-ray images, processed in labelImg, were trained and tested in the Yolo algorithm.
The model was successful in detecting and numbering both primary and permanent teeth on pediatric panoramic radiographs with the mean average precision (mAP) value of 92.22 %, mean average recall (mAR) value of 94.44% and weighted-F1 score of 0.91. The proposed CNN method yielded high and fast performance for automated tooth detection and numbering on pediatric panoramic radiographs. Automatic tooth detection could help dental practitioners to save time and also use it as a pre-processing tool for detection of dental pathologies.
在本文中，我们旨在评估一种深度学习系统在儿童全景 X 光片上自动检测和编号牙齿的性能。
使用基于卷积神经网络（CNN）的 YOLO V4 对象检测模型进行自动牙齿检测和编号。在 labelImg 中处理的 4545 张儿科全景 X 射线图像在 Yolo 算法中进行了训练和测试。
该模型成功地检测和编号了儿童全景 X 光片中的乳牙和恒牙，平均精度（mAP）值为 92.22%，平均召回率（mAR）值为 94.44%，加权-F1 得分为 0.91。所提出的 CNN 方法在儿童全景 X 光片上的自动牙齿检测和编号方面具有高效快速的性能。自动牙齿检测可以帮助牙科医生节省时间，也可以将其用作检测牙科疾病的预处理工具。
