目的:错牙合畸形已成为新兴的全球公共卫生问题。患有前交叉咬伤的人面临着表现出诸如凹形面部轮廓等特征的风险增加,负超喷,咀嚼效率差。针对这个问题,我们提出了一种基于卷积神经网络(CNN)的模型,用于口内图像和视频的自动检测和分类。
方法:本研究共纳入1865例口内图像,其中1493(80%)用于培训,372(20%)用于测试CNN。此外,我们在10个视频上测试了模型,跨越124秒的累积持续时间。为了评估我们预测的表现,指标,包括准确性,灵敏度,特异性,精度,F1分数,精确率-召回率(AUPR)曲线下的面积,采用受试者工作特征(ROC)曲线下面积(AUC)。
结果:训练后的模型表现出值得称道的分类性能,达到0.965的精度和0.986的AUC。此外,它表现出优异的特异性(0.992vs.0.978和0.956,P<0.05)与两名正畸医生的评估相比。相反,CNN模型显示灵敏度降低(0.89vs.0.96和0.92,P<0.05)相对于正畸医生。值得注意的是,CNN模型实现了完美的分类率,成功识别测试集中100%的视频。
结论:深度学习(DL)模型在通过口内图像和视频识别前牙咬合方面表现出卓越的分类准确性。这种熟练程度有可能加快检测严重的咬合不正,促进及时分类以进行适当的治疗,因此,降低并发症的风险。
OBJECTIVE: Malocclusion has emerged as a burgeoning global public health concern. Individuals with an anterior crossbite face an elevated risk of exhibiting characteristics such as a concave facial profile, negative overjet, and poor masticatory efficiency. In response to this issue, we proposed a convolutional neural network (CNN)-based model designed for the automated detection and classification of intraoral images and videos.
METHODS: A total of 1865 intraoral images were included in this study, 1493 (80%) of which were allocated for training and 372 (20%) for testing the CNN. Additionally, we tested the models on 10 videos, spanning a cumulative duration of 124 seconds. To assess the performance of our predictions, metrics including accuracy, sensitivity, specificity, precision, F1-score, area under the precision-recall (AUPR) curve, and area under the receiver operating characteristic (ROC) curve (AUC) were employed.
RESULTS: The trained model exhibited commendable classification performance, achieving an accuracy of 0.965 and an AUC of 0.986. Moreover, it demonstrated superior specificity (0.992 vs. 0.978 and 0.956, P < 0.05) in comparison to assessments by two orthodontists. Conversely, the CNN model displayed diminished sensitivity (0.89 vs. 0.96 and 0.92, P < 0.05) relative to the orthodontists. Notably, the CNN model accomplished a perfect classification rate, successfully identifying 100% of the videos in the test set.
CONCLUSIONS: The deep learning (DL) model exhibited remarkable classification accuracy in identifying anterior crossbite through both intraoral images and videos. This proficiency holds the potential to expedite the detection of severe malocclusions, facilitating timely classification for appropriate treatment and, consequently, mitigating the risk of complications.