Artificial intelligence-based retinal vascular fractal dimension quantification and related factors: A retrospective study

Background: Fractal dimension (Df) quantifies vascular network complexity and spatial filling density, providing a non-invasive biomarker for microvascular pathology assessment. We aimed to establish an artificial intelligence (AI)–powered framework for automated Df quantification and investigate its clinical implications in a general population of the Beijing Eye Study. Methods: This retrospective study utilized data from the Beijing Eye Study 2011. Fundus images meeting quality criteria were processed with an AI algorithm to segment retinal vasculature and calculate Df. Multiple linear and logistic regression models were used to assess the associations of retinal vascular Df with systemic and ocular parameters. Results: AI-based quantification of retinal vascular Df was successfully performed in 3,298 participants (95.1% of 3,468 participants), demonstrating high model performance: segmentation accuracy (0.9660), sensitivity (0.8879), specificity (0.9743), and intersection-over-union (IoU) (0.7110). The mean Df was 1.51 ± 0.09 (median: 1.53; interquartile range: 1.50–1.55). In multiple linear regression analysis, reduced Df was significantly associated with older age (standardized regression coefficient (sβ) = −2.346; P < 0.001), higher systolic blood pressure (sβ = −0.341; P < 0.001), smaller hip circumference (sβ = 0.518; P = 0.014), lower equivalent diopter (sβ = 3.589; P < 0.001), thinner retinal nerve fiber layer thickness (sβ = 0.390; P = 0.002), smaller arteriovenous ratio (sβ = 524.590; P < 0.001), and thinner subfoveal choroidal thickness (sβ = 0.071; P < 0.001). In the logistic regression model, the risk prevalence of hypertension increased with the decrease in Df (OR = 0.853, 95% CI: 0.651–0.801). Conclusion: AI-powered Df analysis may be used as a novel quantitative platform for characterizing retinal microvascular morphological alterations.