Microtia is a congenital abnormality varying from slightly structural abnormalities to the complete absence of the external ear. However, there is no gold standard for assessing the severity of microtia.
The purpose of this study was to develop and test models of artificial intelligence to assess the severity of microtia using clinical photographs.
A total of 800 ear images were included, and randomly divided into training, validation, and test set. Nine convolutional neural networks (CNNs) were trained for classifying the severity of microtia. The evaluation metrics, including accuracy, precision, recall, F1 score, receiver operating characteristic curve, and area under the curve (AUC) values, were used to evaluate the performance of the models.
Eight CNNs were tested with accuracy greater than 0.8. Among them, Alexnet and Mobilenet achieved the highest accuracy of 0.9. Except for Mnasnet, all CNNs achieved high AUC values higher than 0.9 for each grade of microtia. In most CNNs, the grade I microtia had the lowest AUC values and the normal ear had the highest AUC values.
CNN can classify the severity of microtia with high accuracy. Artificial intelligence is expected to provide an objective, automated assessment of the severity of microtia.