Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

TL;DR

This survey reviews AI-based methods for detecting and grading diabetic retinopathy using fundus and OCT images, covering literature from 2016 to 2021 and providing a comprehensive list of datasets.

Contribution

It offers a systematic overview of recent AI approaches for DR detection and grading, including classification, segmentation, and hybrid techniques, along with datasets used in the field.

Findings

114 articles reviewed from 2016-2021

43 datasets compiled for DR research

AI methods show promising accuracy in DR detection

Abstract

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.