Detection of Fuchs’ Uveitis Syndrome From Slit-Lamp Images Using Deep Convolutional Neural Networks in a Chinese Population

摘要

Fuchs’ uveitis syndrome (FUS) is one of the most under- or misdiagnosed uveitis entities. Many undiagnosed FUS patients are unnecessarily overtreated with anti-inflammatory drugs, which may lead to serious complications. To offer assistance for ophthalmologists in the screening and diagnosis of FUS, we developed six deep convolutional neural networks (DCNNs) to detect FUS using slit-lamp images. We also proposed a new optimized model with a mixed “attention” module to improve test accuracy. In the same independent set, we compared the performance between these DCNNs and ophthalmologists in detecting FUS. Six different network models, including Resnet50, SE-Resnet50, ResNext50, SE-ResNext50, ST-ResNext50 and Xception, were used to predict FUS automatically with the area under the receiver operating characteristic curves (AUCs) that ranged from 0.951 to 0.977. Our proposed SET-ResNext50 model (accuracy = 0.930; Precision = 0.918; Recall = 0.923; F1-measure = 0.920) with an AUC of 0.977 consistently outperformed the other networks and outperformed general ophthalmologists by a large margin. Heat map visualizations of the SET-ResNext50 were produced to identify the target areas in the slit-lamp images. In conclusion, we confirmed a trained classification method based on DCNNs achieved high effectiveness in distinguishing Fuchs from other forms of anterior uveitis. The performance of the DCNNs was better than that of general ophthalmologists and could be of value in FUS diagnosis.