Deep Learning-based Assessment of Facial Asymmetry Using U-Net Deep Convolutional Neural Network Algorithm.

OBJECTIVES

This study aimed to evaluate the diagnostic performance of a deep convolutional neural network (DCNN)-based computer-assisted diagnosis (CAD) system to detect facial asymmetry on posteroanterior (PA) cephalograms and compare the results of the DCNN with those made by the orthodontist.

MATERIALS AND

METHODS

PA cephalograms of 1020 patients with orthodontics were used to train the DCNN-based CAD systems for autoassessment of facial asymmetry, the degree of menton deviation, and the coordinates of its regarding landmarks. Twenty-five PA cephalograms were used to test the performance of the DCNN in analyzing facial asymmetry. The diagnostic performance of the DCNN-based CAD system was assessed using independent t -tests and Bland-Altman plots.

RESULTS

Comparison between the DCNN-based CAD system and conventional analysis confirmed no significant differences. Bland-Altman plots showed good agreement for all the measurements.

CONCLUSIONS

The DCNN-based CAD system might offer a clinically acceptable diagnostic evaluation of facial asymmetry on PA cephalograms.