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

ABSTRACT

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 artery-vein 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.