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

TL;DR

This study evaluates deep learning algorithms, specifically U-Net and fully convolutional neural networks, for detecting anatomical reference points on head radiological images, comparing their accuracy to orthodontists' results.

Contribution

It demonstrates that U-Net outperforms standard fully convolutional networks in accuracy, closely matching orthodontists' detection performance.

Findings

U-Net achieves higher accuracy than fully convolutional networks.

U-Net results are comparable to orthodontists' detection.

Deep learning can effectively automate anatomical point detection.

Abstract

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.