Kaya Emine, Gunec Huseyin Gurkan, Aydin Kader Cesur, Urkmez Elif Seyda, Duranay Recep, Ates Hasan Fehmi
Department of Pediatric Dentistry, Faculty of Dentistry, Istanbul Okan University, Istanbul, Turkey.
Department of Endodontics, Faculty of Dentistry, Atlas University, Istanbul, Turkey.
Imaging Sci Dent. 2022 Sep;52(3):275-281. doi: 10.5624/isd.20220050. Epub 2022 Jul 5.
The aim of this study was to assess the performance of a deep learning system for permanent tooth germ detection on pediatric panoramic radiographs.
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.
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.
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.
本研究旨在评估一种深度学习系统在儿童全景X线片上检测恒牙胚的性能。
共收集了4518张5至13岁儿童的匿名全景X线片。基于卷积神经网络（CNN）的目标检测模型YOLOv4用于自动检测恒牙胚。在LabelImg中处理的儿童全景图像在YOLOv4算法中进行训练和测试。计算真阳性率、假阳性率和假阴性率。使用混淆矩阵评估模型的性能。
在儿童全景X线片上检测恒牙胚的YOLOv4模型的平均精度值为94.16%，F1值为0.90，具有高度显著性。YOLOv4的平均推理时间为90毫秒。
使用基于深度学习的方法在儿童全景X线片上检测恒牙胚，可能有助于早期诊断牙齿缺失或多生牙，并帮助牙科医生找到更准确的治疗方案，同时节省时间和精力。
