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Retinal vessels reflect alterations related to hypertension and arteriosclerosis in the physical status. Previously, we had reported a deep-learning algorithm for automatically detecting retinal vessels and measuring the total retinal vascular area in fundus photographs (VAFP). Herein, we investigated the relationship between VAFP and brachial–ankle pulse wave velocity (baPWV), which is the gold standard for arterial stiffness assessment in clinical practice. Retinal photographs ( n  = 696) obtained from 372 individuals who visited the Keijinkai Maruyama Clinic for regular health checkups were used to analyze VAFP. Additionally, the baPWV was measured for each patient. Automatic retinal-vessel segmentation was performed using our deep-learning algorithm, and the total arteriolar area (AA) and total venular area (VA) were measured. Correlations between baPWV and several parameters, including AA and VA, were assessed. The baPWV was negatively correlated with AA ( R  = –0.40,  n  = 696,  P  &lt; 2.2e-16) and VA ( R  = –0.36,  n  = 696,  P  &lt; 2.2e-16). Independent variables (AA, sex, age, and systolic blood pressure) selected using the stepwise method showed a significant correlation with baPWV. The estimated baPWV, calculated using a regression equation with variables including AA, showed a better correlation with the measured baPWV ( R  = 0.70,  n  = 696,  P  &lt; 2.2e-16) than the estimated value without AA ( R  = 0.68,  n  = 696,  P  &lt; 2.2e-16). AA and VA were significantly correlated with baPWV. Moreover, baPWV estimated using AA correlated well with the actual baPWV. VAFP may serve as an alternative biomarker for evaluating systemic arterial stiffness.

Relationship between Brachial–Ankle Pulse Wave Velocity and Fundus Arteriolar Area Calculated Using a Deep-Learning Algorithm