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.