Diabetic Retinopathy (DR) is a leading cause of vision loss in the world,. In\nthe past few Diabetic Retinopathy (DR) is a leading cause of vision loss in the\nworld. In the past few years, Artificial Intelligence (AI) based approaches\nhave been used to detect and grade DR. Early detection enables appropriate\ntreatment and thus prevents vision loss, Both fundus and optical coherence\ntomography (OCT) images are used to image the retina. With deep\nlearning/machine learning apprroaches it is possible to extract features from\nthe images and detect the presence of DR. Multiple strategies are implemented\nto detect and grade the presence of DR using classification, segmentation, and\nhybrid techniques. This review covers the literature dealing with AI approaches\nto DR that have been published in the open literature over a five year span\n(2016-2021). In addition a comprehensive list of available DR datasets is\nreported. Both the PICO (P-patient, I-intervention, C-control O-outcome) and\nPreferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA)2009\nsearch strategies were employed. We summarize a total of 114 published articles\nwhich conformed to the scope of the review. In addition a list of 43 major\ndatasets is presented.\n