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

Wanyun Zhang,Qingfeng Cao,Guannan Su,Zhijun Chen,Lin Chen,Yao Wang,Peizeng Yang,Rui Chang,Ying Zhu,Chunjiang Zhou,Han Zhang

doi:10.3389/fcell.2021.684522

Abstract

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 seven 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. Seven different network models, including Xception, Resnet50, SE-Resnet50, ResNext50, SE-ResNext50, ST-ResNext50, and SET-ResNext50, 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 provided to identify the target areas in the slit-lamp images. In conclusion, we confirmed that a trained classification method based on DCNNs achieved high effectiveness in distinguishing FUS from other forms of anterior uveitis. The performance of the DCNNs was better than that of general ophthalmologists and could be of value in the diagnosis of FUS.

Highlights

Fuchs’ uveitis syndrome (FUS) is a chronic, mostly unilateral, non-granulomatous anterior uveitis, accounting for 1–20% of all cases of uveitis at referral centers, and is the second most common form of non-infectious uveitis (Yang et al, 2006; Kazokoglu et al, 2008; Abano et al, 2017)
To offer assistance for ophthalmologists in the screening and diagnosis of FUS, we decided to develop deep convolutional neural networks (DCNNs) to classify slit-lamp images automatically and in this report we show its feasibility in the detection of FUS
SET-ResNext50 with its area under the receiver operating characteristic curves (AUCs) of 0.977 showed that this model could be the optimal choice to facilitate the diagnosis of FUS among seven networks

Summary

Introduction

Fuchs’ uveitis syndrome (FUS) is a chronic, mostly unilateral, non-granulomatous anterior uveitis, accounting for 1–20% of all cases of uveitis at referral centers, and is the second most common form of non-infectious uveitis (Yang et al, 2006; Kazokoglu et al, 2008; Abano et al, 2017). In a previous study on Chinese FUS patients, we described the presence of varying degrees of diffuse iris depigmentation without posterior synechiae rather than heterochromia (Yang et al, 2006). Degrees of diffuse iris depigmentation may be considered as the most sensitive and reliable signs of FUS in Chinese as well as in other highly pigmented populations (Mohamed and Zamir, 2005; Yang et al, 2006). The diagnosis is highly dependent on the skills of the uveitis specialist with broad experience in the detection of subtle iris pigmentation abnormalities in a patient with mild anterior uveitis

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Discussion

Conclusion