Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis. This review presents the application of convolutional neural networks in one of the fields of dentistry - orthodontics. Advances in medical imaging technologies and methods allow CNNs to be used in orthodontics to shorten the planning time of orthodontic treatment, including an automatic search of landmarks on cephalometric X-ray images, tooth segmentation on Cone-Beam Computed Tomography (CBCT) images or digital models, and classification of defects on X-Ray panoramic images. In this work, we describe the current methods, the architectures of deep convolutional neural networks used, and their implementations, together with a comparison of the results achieved by them. The promising results and visualizations of the described studies show that the use of methods based on convolutional neural networks allows for the improvement of computer-based orthodontic treatment planning, both by reducing the examination time and, in many cases, by performing the analysis much more accurately than a manual orthodontist does.
翻译:计算机视觉的许多领域,如物体跟踪和识别、安全、军事和生物医学图像分析,都使用进化神经网络(CNNs),这一审查展示了在牙科 -- -- 矫形术领域应用进化神经网络的情况。医学成像技术和方法的进步使得CNN能够用于矫形术,缩短矫形治疗的规划时间,包括自动搜索胸腔X射线图像的标志、Cone-Beam复合成像或数字模型上的牙分解,以及X射线全景图像的缺陷分类。在这项工作中,我们描述了目前使用的方法、深进化神经网络的结构及其实施情况,并比较了它们取得的结果。所述研究的有希望的结果和视觉化表明,使用进化神经网络的方法可以改进计算机或成形处理规划,既缩短了检查时间,也在许多情况下,以手动或手动方式进行了比更精确的分析。