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

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          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.

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          Deep Residual Learning for Image Recognition

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            ImageNet: A large-scale hierarchical image database

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              Xception: Deep Learning with Depthwise Separable Convolutions

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                Author and article information

                Contributors
                Journal
                Front Cell Dev Biol
                Front Cell Dev Biol
                Front. Cell Dev. Biol.
                Frontiers in Cell and Developmental Biology
                Frontiers Media S.A.
                2296-634X
                18 June 2021
                2021
                : 9
                : 684522
                Affiliations
                [1] 1The First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Ophthalmology and Chongqing Eye Institute, Chongqing Branch of National Clinical Research Center for Ocular Diseases , Chongqing, China
                [2] 2School of Computer Science and Technology, Harbin Institute of Technology , Harbin, China
                Author notes

                Edited by: Wei Chi, Sun Yat-sen University, China

                Reviewed by: Monica Trif, Centre for Innovative Process Engineering, Germany; Zuhong He, Wuhan University, China

                *Correspondence: Peizeng Yang, peizengycmu@ 123456126.com

                These authors have contributed equally to this work

                This article was submitted to Molecular Medicine, a section of the journal Frontiers in Cell and Developmental Biology

                Article
                10.3389/fcell.2021.684522
                8250145
                34222252
                7c2ae92a-4233-4ddc-a679-33ef19fee1e5
                Copyright © 2021 Zhang, Chen, Zhang, Su, Chang, Chen, Zhu, Cao, Zhou, Wang and Yang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 23 March 2021
                : 30 April 2021
                Page count
                Figures: 4, Tables: 2, Equations: 0, References: 37, Pages: 8, Words: 0
                Categories
                Cell and Developmental Biology
                Original Research

                fuchs’ uveitis syndrome,diffuse iris depigmentation,slit-lamp images,deep convolutional neural model,deep learning

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