Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, 101 Daehakro, Jongro-gu, 03080, Seoul, Korea.
Department of Orthodontics, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, 08308, Seoul, Korea.
BMC Oral Health. 2022 Oct 25;22(1):454. doi: 10.1186/s12903-022-02466-x.
Taking facial and intraoral clinical photos is one of the essential parts of orthodontic diagnosis and treatment planning. Among the diagnostic procedures, classification of the shuffled clinical photos with their orientations will be the initial step while it was not easy for a machine to classify photos with a variety of facial and dental situations. This article presents a convolutional neural networks (CNNs) deep learning technique to classify orthodontic clinical photos according to their orientations.
To build an automated classification system, CNNs models of facial and intraoral categories were constructed, and the clinical photos that are routinely taken for orthodontic diagnosis were used to train the models with data augmentation. Prediction procedures were evaluated with separate photos whose purpose was only for prediction.
Overall, a 98.0% valid prediction rate resulted for both facial and intraoral photo classification. The highest prediction rate was 100% for facial lateral profile, intraoral upper, and lower photos.
An artificial intelligence system that utilizes deep learning with proper training models can successfully classify orthodontic facial and intraoral photos automatically. This technique can be used for the first step of a fully automated orthodontic diagnostic system in the future.
拍摄面部和口腔临床照片是正畸诊断和治疗计划的基本组成部分之一。在诊断程序中，对杂乱无章的临床照片进行分类并确定其方向是初始步骤，但对于机器来说，对具有各种面部和牙齿情况的照片进行分类并不容易。本文提出了一种卷积神经网络（CNN）深度学习技术，根据其方向对正畸临床照片进行分类。
为了构建自动分类系统，构建了面部和口腔类别的 CNN 模型，并使用数据增强对常规用于正畸诊断的临床照片进行训练。预测过程是使用仅用于预测的单独照片进行评估的。
总体而言，面部和口腔照片分类的有效预测率达到了 98.0%。面部侧位、口腔上、下照片的预测率最高，达到了 100%。
利用深度学习和适当的训练模型的人工智能系统可以成功地自动对正畸面部和口腔照片进行分类。这项技术可用于未来全自动正畸诊断系统的第一步。
