Managing Diabetic Retinopathy With Deep Learning: A Data Centric Overview

ABSTRACT Diabetic retinopathy (DR) is a serious microvascular complication of diabetes, and one of the leading causes of vision loss worldwide. Although automated detection and grading, with deep learning (DL), can reduce the burden on ophthalmologists, it is constrained by the limited availability of high‐quality datasets. Existing repositories often remain geographically narrow, contain limited samples, and exhibit inconsistent annotations or variable image quality; thereby, restricting their clinical reliability. This paper presents a comprehensive review and comparative analysis of fundus image datasets used in the management of DR. It introduces a taxonomy to organize the DR datasets and categorize them by size, accessibility, and annotation type (such as image‐level, lesion‐level, and multi‐disease). The study also evaluates their usability across key tasks, including binary classification, severity grading, lesion localization, and multi‐disease screening, based on an evaluation framework. A recently published dataset is presented as a case study to illustrate broader challenges in dataset curation and usage. Finally, this paper briefly discusses the recent data‐centric advancements, developments in deep learning models, and the impact of integrating clinical along with fundus images for automated screening. The review consolidates current knowledge while highlighting persistent gaps such as the lack of standardized lesion‐level annotations and longitudinal data. It also outlines recommendations for future dataset development to support clinically reliable and explainable solutions in DR screening.