Artificial intelligence for assessing the severity of microtia via deep convolutional neural networks.

ABSTRACT:

Background

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.

Objectives

The purpose of this study was to develop and test models of artificial intelligence to assess the severity of microtia using clinical photographs.

Methods

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

SUBMITTER: Wang D 

PROVIDER: S-EPMC9492961 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature