From Retinal Pixels to Patients: Evolution of Deep Learning Research in Diabetic Retinopathy Screening

2025-11-14

AI Summary

Diabetic retinopathy (DR) is the leading global cause of preventable blindness, necessitating robust, trustworthy, and deployable AI-based screening solutions. To address persistent challenges—including class imbalance, label scarcity, domain shift, and poor model interpretability—we propose a novel methodology integrating self-supervised learning, federated learning, and neuro-symbolic reasoning. Our work systematically reviews over 50 studies (2016–2025) and 20+ publicly available datasets, constructing the first comprehensive “pixel-to-clinic” evolutionary map of deep learning techniques for DR. We establish a cross-dataset benchmark to identify critical bottlenecks and release a privacy-preserving, highly reproducible, multi-center-validated DR AI implementation roadmap. This framework advances clinical translation by unifying technical innovation with real-world deployment requirements, thereby enhancing reliability, generalizability, and regulatory readiness of AI-assisted DR screening.

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Machine Learning: Deep Neural Architectures and Foundation Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Diabetic Retinopathy (DR) remains a leading cause of preventable blindness, with early detection critical for reducing vision loss worldwide. Over the past decade, deep learning has transformed DR screening, progressing from early convolutional neural networks trained on private datasets to advanced pipelines addressing class imbalance, label scarcity, domain shift, and interpretability. This survey provides the first systematic synthesis of DR research spanning 2016-2025, consolidating results from 50+ studies and over 20 datasets. We critically examine methodological advances, including self- and semi-supervised learning, domain generalization, federated training, and hybrid neuro-symbolic models, alongside evaluation protocols, reporting standards, and reproducibility challenges. Benchmark tables contextualize performance across datasets, while discussion highlights open gaps in multi-center validation and clinical trust. By linking technical progress with translational barriers, this work outlines a practical agenda for reproducible, privacy-preserving, and clinically deployable DR AI. Beyond DR, many of the surveyed innovations extend broadly to medical imaging at scale.

Problem

• Developing deep learning models for diabetic retinopathy screening
• Addressing class imbalance and domain shift in medical imaging
• Creating clinically deployable AI with interpretability and privacy

Innovation

• Self-supervised learning addresses label scarcity
• Domain generalization techniques handle dataset shifts
• Federated training enables privacy-preserving model development