  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.
