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