Exploring the effect of hypertension on retinal microvasculature using deep learning on East Asian population

Hypertension is the leading risk factor of cardiovascular disease and has profound effects on both the structure and function of the microvasculature. Abnormalities of the retinal vasculature may reflect the degree of microvascular damage due to hypertension, and these changes can be detected with fundus photographs. This study aimed to use deep learning technique that can detect subclinical features appearing below the threshold of a human observer to explore the effect of hypertension on morphological features of retinal microvasculature. We collected 2012 retinal photographs which included 1007 from patients with a diagnosis of hypertension and 1005 from normotensive control. By method of vessel segmentation, we removed interference information other than retinal vasculature and contained only morphological information about blood vessels. Using these segmented images, we trained a small convolutional neural networks (CNN) classification model and used a deep learning technique called Gradient-weighted Class Activation Mapping (Grad-CAM) to generate heat maps for the class “hypertension”. Our model achieved an accuracy of 60.94%, a specificity of 51.54%, a precision of 59.27%, and a recall of 70.48%. The AUC was 0.6506. In the heat maps for the class “hypertension”, red patchy areas were mainly distributed on or around arterial/venous bifurcations. This indicated that the model has identified these regions as being the most important for predicting hypertension. Our study suggested that the effect of hypertension on retinal microvascular morphology mainly occurred at branching of vessels. The change of the branching pattern of retinal vessels was probably the most significant in response to elevated blood pressure.