Analysis and comparison of retinal vascular parameters under different glucose metabolic status based on deep learning  

作　　者：Yan Jiang Di Gong Xiao-Hong Chen Lin Yang Jing-Jing Xu Qi-Jie Wei Bin-Bin Chen Yong-Jiang Cai Wen-Qun Xi Zhe Zhang 

机构地区：[1]Departments of Laboratory Medicine,the Second Affiliated Hospital,Zhejiang University School of Medicine,Hangzhou 310009,Zhejiang Province,China [2]Shenzhen Eye Hospital,Jinan University,Shenzhen 518040,Guangdong Province,China [3]Center of Health Management,Peking University Shenzhen Hospital,Shenzhen 518036,Guangdong Province,China [4]Visionary Intelligence Ltd.,Beijing 100080,China [5]Ophthalmology Center,the Second Affiliated Hospital,Zhejiang University School of Medicine,Hangzhou 310009,Zhejiang Province,China

出　　处：《International Journal of Ophthalmology(English edition)》2024年第9期1581-1591,共11页国际眼科杂志（英文版）

基　　金：Supported by Shenzhen Science and Technology Program(No.JCYJ20220530153604010).

摘　　要：AIM:To develop a deep learning-based model for automatic retinal vascular segmentation,analyzing and comparing parameters under diverse glucose metabolic status(normal,prediabetes,diabetes)and to assess the potential of artificial intelligence(AI)in image segmentation and retinal vascular parameters for predicting prediabetes and diabetes.METHODS:Retinal fundus photos from 200 normal individuals,200 prediabetic patients,and 200 diabetic patients(600 eyes in total)were used.The U-Net network served as the foundational architecture for retinal arteryvein segmentation.An automatic segmentation and evaluation system for retinal vascular parameters was trained,encompassing 26 parameters.RESULTS:Significant differences were found in retinal vascular parameters across normal,prediabetes,and diabetes groups,including artery diameter(P=0.008),fractal dimension(P=0.000),vein curvature(P=0.003),C-zone artery branching vessel count(P=0.049),C-zone vein branching vessel count(P=0.041),artery branching angle(P=0.005),vein branching angle(P=0.001),artery angle asymmetry degree(P=0.003),vessel length density(P=0.000),and vessel area density(P=0.000),totaling 10 parameters.CONCLUSION:The deep learning-based model facilitates retinal vascular parameter identification and quantification,revealing significant differences.These parameters exhibit potential as biomarkers for prediabetes and diabetes.

关 键 词：deep learning retinal vascular parameters segmentation model DIABETES PREDIABETES 

分 类 号：R774.1[医药卫生—眼科]