Evaluation of reconstructed auricles by convolutional neural networks

Abstract

The difficulty in determining which structures are crucial to ensure a natural-looking ear has been plaguing surgeons for many years. This preliminary study explores the feasibility of training convolutional neural network (CNN) models to evaluate a reconstructed auricle as accurate as a human would. By visualizing the attention of trained models, the criteria for the design of a natural-looking auricle can be established.A total of 400 pictures were evaluated by 20 volunteers, and 20 labeled datasets were generated, which were then used to train ResNet models that had been pre-trained on ImageNet. The saliency maps and occlusion maps of each trained model were calculated to capture the attention of models.The average accuracy of the 20 models was 0.8245 ± 0.0356 (>0.80), and the evaluation results of the trained model and the medical student showed a significant correlation (P < 0.05). For the attention visualization of auricles labeled as normal, distribution of the highlighted portions corresponded to a linear contour of the helix, the inferior crura of the antihelix, and the contour of the concha.A CNN can provide an evaluation of a reconstructed auricle in a manner similar to that of a medical student. Saliency maps generated by the CNN demonstrate the subjective view, which was consistent with professional opinion.