With the emergence in modern dentistry, the study of dental occlusion has been a subject of major interest. The aim of the present study is to investigate the capabilities of deep learning for the classification of dental occlusion using 3D images that has an exciting impact in several fields of dental anatomy. In present work, the 3D stereolithography (STL) files depicting the dental structures are converted to 2D histograms, using Absolute Angle Shape Distribution (AAD) technique, which are used as an input to deep or machine learning models for classification of dental structures based on the similarity of their shape features. To the best of the authors’ knowledge, no solution has been proposed for classification of dental occlusion using deep learning. Thus, an attempt has been made to propose a classification technique for dental occlusion. Based on the experimental analysis, it has been revealed that the deep learning-based convolutional neural network along with AAD performs better as compared to other existing machine learning techniques, with maximum accuracy of 78.95% for occlusion classification. However, the presented study is preliminary, but the experimental outcomes have demonstrated that deep learning is helpful in classifying dental occlusion and it has great application potential in the computer-assisted orthodontic treatment diagnosis.

中文翻译：

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OCLU-NET用于3D牙齿模型的咬合分类

随着现代牙科的兴起，对牙合的研究已成为人们关注的主题。本研究的目的是研究使用3D图像对牙齿咬合进行分类的深度学习功能，该功能在牙齿解剖学的多个领域都具有令人兴奋的影响。在当前工作中，使用绝对角度形状分布（AAD）技术将描述牙齿结构的3D立体光刻（STL）文件转换为2D直方图，该技术用作深度或机器学习模型的输入，以基于以下内容对牙齿结构进行分类它们的形状特征相似。据作者所知，尚未提出使用深度学习对牙合进行分类的解决方案。因此，已经尝试提出一种用于牙咬合的分类技术。根据实验分析，已发现基于深度学习的卷积神经网络以及AAD与其他现有机器学习技术相比表现更好，遮挡分类的最大准确性为78.95％。然而，本研究是初步的，但是实验结果表明深度学习有助于对牙合进行分类，并且在计算机辅助正畸治疗诊断中具有巨大的应用潜力。