Intelligent diagnosis of retinal vein occlusion based on color fundus photographs  

作　　者：Yu-Ke Ji Rong-Rong Hua Sha Liu Cui-Juan Xie Shao-Chong Zhang Wei-Hua Yang 

机构地区：[1]Eye Hospital,Nanjing Medical University,Nanjing 210000,Jiangsu Province,China [2]College of Electronic Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210000,Jiangsu Province,China [3]Shenzhen Eye Institute,Shenzhen Eye Hospital,Jinan University,Shenzhen 518000,Guangdong Province,China

出　　处：《International Journal of Ophthalmology(English edition)》2024年第1期1-6,共6页国际眼科杂志（英文版）

基　　金：Supported by Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties(No.SZGSP014);Sanming Project of Medicine in Shenzhen(No.SZSM202011015);Shenzhen Science and Technology Planning Project(No.KCXFZ20211020163813019).

摘　　要：AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of RVO.All the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease experts.Swin Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were conducted.The model’s performance was compared to that of the experts.RESULTS:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 respectively.In addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,respectively.The diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was superior.CONCLUSION:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

关 键 词：deep learning artificial intelligence Swin Transformer diagnostic model retinal vein occlusion color fundus photographs 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] R774.1[自动化与计算机技术—控制科学与工程]