Published Paper Details:

Hypertension and heart attack 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 heart attack, and these changes can be detected with fundus photographs. This project aimed to use deep learning and AI technique that can detect subclinical features appearing below the threshold of a human observer to explore the effect of hypertension and heart attack on morphological features of retinal microvasculature. retinal photographs are collected from data science domain with a diagnosis of hypertension and heart attack. 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 to generate heat maps for the class "hypertension" and "Heart attack". The main features are 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 and little similar for heart attack.