Andrey Gaidel

Alexander Kolsanov, Nikolai Popov, Irina Aiupova et al.

This article describes the goals and objectives of developing a software complex for planning the treatment of dentoalveolar anomalies, the architecture of the software complex as interacting components for treatment planning, as well as the principle of using algorithms using convolutional neural networks within the software complex for a component that solves the problem of decoding a teleradiographic image.

Konstantin Dobratulin, Andrey Gaidel, Irina Aupova et al.

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.

N. S. Shlyankin, A. V. Gaidel

The application of the hidden Markov model with various parameters in the segmentation task of QRS, ST, T, P, PQ, ISO complexes of electrocardiograms is considered. Models were trained using the Viterbi algorithm using the QT Database. For comparison, the Pan-Tompkins algorithm for searching for the duration of QRS complexes was modified.