Cardiovascular disease (CVD) is ranked 1st for mortality rate globally. In 2016, approximately 17.9 million people died from CVDs, representing 31% of all global deaths.~Retinal vasculature has been characterised as the 'window' to the body's circulatory system and been correlated with several diseases that perturbate systemic micro- and macro-vasculature such as hypertension, stroke and chronic kidney disease.~As microvascular changes often precede macrovascular changes, retinal imaging has the capability to be an easily available, non-invasive biomarker to screen for multiple vascular pathologies. A deep learning system (DLS) is a novel,state-of-art artificial intelligence (AI) technology that has achieved robust diagnostic performance for medical imaging analysis. The integration of deep learning (DL) into retinal image evaluation has accelerated its potential further. Its examination of fundus photographs has demonstrated abilities to detect several signs that are undetected by the human eye. With recent correlations being established between CAD and retinal vasculature aberrancies such as reduced calibres and fractal dimensions, there is a clear niche for AI to predict cardiac diseases with the use of retinal fundus photographs.
The main purpose of this study is to pioneer an easy risk stratification tools, which is developed using novel artificial intelligence (AI) algorithms, that will be able to detect common and fatal heart diseases easily simply through a picture of the back of the eye, the retina. The retinal images will be analysed using a computer application with the risk stratification tool to predict health outcome of individual. The study also aims to correlate between clinical characteristics, lifestyle (eg. exercise, sleep, erectile dysfunction) and diet to retinal and coronary vasculature and clinical outcomes.