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

The efficiency of deep learning algorithms for detecting anatomical reference points on radiological images of the head profile