Chen Ivane Delos Santos, Yang Chieh-Ming, Chen Mei-Juan, Chen Ming-Chin, Weng Ro-Min, Yeh Chia-Hung
Department of Electrical Engineering, National Dong Hwa University, Hualien 97401, Taiwan.
Department of Electrical Engineering, National Taiwan Normal University, Taipei 10610, Taiwan.
Bioengineering (Basel). 2023 Aug 1;10(8):911. doi: 10.3390/bioengineering10080911.
Dental X-ray images are important and useful for dentists to diagnose dental diseases. Utilizing deep learning in dental X-ray images can help dentists quickly and accurately identify common dental diseases such as periodontitis and dental caries. This paper applies image processing and deep learning technologies to dental X-ray images to propose a simultaneous recognition method for periodontitis and dental caries. The single-tooth X-ray image is detected by the YOLOv7 object detection technique and cropped from the periapical X-ray image. Then, it is processed through contrast-limited adaptive histogram equalization to enhance the local contrast, and bilateral filtering to eliminate noise while preserving the edge. The deep learning architecture for classification comprises a pre-trained EfficientNet-B0 and fully connected layers that output two labels by the sigmoid activation function for the classification task. The average precision of tooth detection using YOLOv7 is 97.1%. For the recognition of periodontitis, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve is 98.67%, and the AUC of the precision-recall (PR) curve is 98.38%. For the recognition of dental caries, the AUC of the ROC curve is 98.31%, and the AUC of the PR curve is 97.55%. Different from the conventional deep learning-based methods for a single disease such as periodontitis or dental caries, the proposed approach can provide the recognition of both periodontitis and dental caries simultaneously. This recognition method presents good performance in the identification of periodontitis and dental caries, thus facilitating dental diagnosis.
牙科X光图像对于牙医诊断牙科疾病非常重要且有用。在牙科X光图像中运用深度学习可以帮助牙医快速准确地识别常见的牙科疾病，如牙周炎和龋齿。本文将图像处理和深度学习技术应用于牙科X光图像，提出了一种牙周炎和龋齿的同时识别方法。通过YOLOv7目标检测技术检测单颗牙齿的X光图像，并从根尖X光图像中裁剪出来。然后，通过对比度受限自适应直方图均衡化进行处理以增强局部对比度，并采用双边滤波在保留边缘的同时消除噪声。用于分类的深度学习架构包括预训练的EfficientNet-B0和全连接层，通过sigmoid激活函数输出两个标签以完成分类任务。使用YOLOv7进行牙齿检测的平均精度为97.1%。对于牙周炎的识别，接收者操作特征（ROC）曲线的曲线下面积（AUC）为98.67%，精确率-召回率（PR）曲线的AUC为98.38%。对于龋齿的识别，ROC曲线的AUC为98.31%，PR曲线的AUC为97.55%。与用于单一疾病（如牙周炎或龋齿）的传统深度学习方法不同，所提出的方法可以同时提供牙周炎和龋齿的识别。这种识别方法在牙周炎和龋齿的识别方面表现出良好的性能，从而有助于牙科诊断。
