Quantitative evaluation of retinal vascular morphology based on the human visual bionic mechanism for the evaluation of diabetic retinopathy onset.

BACKGROUND AND AIMS: A limited amount of diabetic retinopathy (DR) development can be explained by traditional risk factors. This study aimed to determine the association of artificial intelligence (AI)-assisted retinal vasculature measurement parameters with DR onset in adults with type 2 diabetes.

METHODS: This observational cohort study was conducted in 556 patients with type 2 diabetes without DR who underwent general and ophthalmological examinations. Their blood pressure, body mass index (BMI), fasting blood glucose (FBG), and glycosylated hemoglobin levels were measured. An AI-based fundus image analysis system was used to assess vessel tortuosity, fractal dimension, and retinal arteriolar/venular diameters in different regions.

RESULTS: At the end of the observation period, 299 patients remained free of DR (control group), whereas 257 developed DR (progression group). The retinal arteriolar caliber, venular caliber, arteriolar tortuosity, and venular tortuosity did not differ significantly between the groups at baseline (P > 0.05). However, DR onset was significantly correlated with retinal arteriolar caliber, fractal dimensions, and retinal venular tortuosity (P < 0.05). The widening of the retinal arteriolar diameter within the 1.5-2.0 PD region of the optical disc center was the strongest predictor of DR development. It also improved the performance of the DR onset prediction model compared with those using traditional risk factors alone.

CONCLUSIONS: AI-assisted retinal vasculature measurements were associated with DR onset and progression. In addition to increased retinal venular tortuosity and fractal dimension, retinal arteriolar caliber within the 1.5-2.0 PD may serve as a valuable biomarker of early vascular dysfunction and increased DR risk.