  In this article we investigate the efficiency of deep learning algorithms in
solving the task of detecting anatomical reference points on radiological
images of the head in lateral projection using a fully convolutional neural
network and a fully convolutional neural network with an extended architecture
for biomedical image segmentation - U-Net. A comparison is made for the results
of detection anatomical reference points for each of the selected neural
network architectures and their comparison with the results obtained when
orthodontists detected anatomical reference points. Based on the obtained
results, it was concluded that a U-Net neural network allows performing the
detection of anatomical reference points more accurately than a fully
convolutional neural network. The results of the detection of anatomical
reference points by the U-Net neural network are closer to the average results
of the detection of reference points by a group of orthodontists.
