Department of Dentistry, College of Medicine, Yeungnam University, Daegu, South Korea.
Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, South Korea.
PLoS One. 2024 Sep 6;19(9):e0310004. doi: 10.1371/journal.pone.0310004. eCollection 2024.
Camera image-based deep learning (DL) techniques have achieved promising results in dental caries screening. To apply the intraoral camera image-based DL technique for dental caries detection and assess its diagnostic performance, we employed the ensemble technique in the image classification task. 2,682 intraoral camera images were used as the dataset for image classification according to dental caries presence and caries-lesion localization using DL models such as ResNet-50, Inception-v3, Inception-ResNet-v2, and Faster R-convolutional neural network according to diagnostic study design. 534 participants whose mean age [SD] was 47.67 [±13.94] years were enrolled. The dataset was divided into training (56.0%), validation (14.0%), and test subset (30.0%) annotated by one experienced dentist as a reference standard about dental caries detection and lesion location. The confusion matrix, area under the receiver operating characteristic curve (AUROC), and average precision (AP) were evaluated for performance analysis. In the end-to-end dental caries image classification, the ensemble DL models had consistently improved performance, in which as the best results, the ensemble model of Inception-ResNet-v2 achieved 0.94 of AUROC and 0.97 of AP. On the other hand, the explainable model achieved 0.91 of AUROC and 0.96 of AP after the ensemble application. For dental caries classification using intraoral camera images, the application of ensemble techniques exhibited consistently improved performance regardless of the DL models. Furthermore, the trial to create an explainable DL model based on carious lesion detection yielded favorable results.
基于相机图像的深度学习（DL）技术在龋齿筛查方面取得了有前景的成果。为了应用基于口腔内相机图像的 DL 技术进行龋齿检测并评估其诊断性能，我们在图像分类任务中采用了集成技术。根据诊断研究设计，使用 ResNet-50、Inception-v3、Inception-ResNet-v2 和 Faster R-卷积神经网络等 DL 模型，根据龋齿存在和龋齿病变定位，将 2682 张口腔内相机图像用作数据集进行图像分类。纳入了 534 名参与者，其平均年龄 [标准差] 为 47.67 [±13.94] 岁。数据集分为训练集（56.0%）、验证集（14.0%）和测试子集（30.0%），由一位经验丰富的牙医作为龋齿检测和病变位置的参考标准进行标注。混淆矩阵、接收器工作特征曲线下的面积（AUROC）和平均精度（AP）用于性能分析。在端到端龋齿图像分类中，集成 DL 模型的性能始终得到了提高，其中，基于 Inception-ResNet-v2 的集成模型的 AUROC 达到了 0.94，AP 达到了 0.97。另一方面，经过集成应用后，可解释模型的 AUROC 达到了 0.91，AP 达到了 0.96。对于使用口腔内相机图像进行龋齿分类，无论 DL 模型如何，应用集成技术都能始终提高性能。此外，尝试基于龋齿病变检测创建可解释的 DL 模型也取得了良好的结果。
