Observational studyNature communications2021
A deep learning system for detecting diabetic retinopathy across the disease spectrum.
Observational study in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 150 papers, 6 of them syntheses that pooled it.
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Who cites it
150 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Detection and Risk Prediction of Diabetes Related Kidney Disease Using Retinal Images: A Systematic Review and Meta-Analysis.Journal of diabetes · 2026Pooled it
- Deep learning models for ICU readmission prediction: a systematic review and meta-analysis.Critical care (London, England) · 2025Pooled it
- Risk prediction models for diabetic retinopathy: a systematic review.Frontiers in endocrinology · 2025Pooled it
- Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature.Frontiers in medicine · 2025Pooled it
- Test accuracy of artificial intelligence-based grading of fundus images in diabetic retinopathy screening: A systematic review.Journal of medical screening · 2023Pooled it
- Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies.Frontiers in endocrinology · 2023Pooled it
- Beyond hyperglycaemia: diabetic retinopathy is a multifactorial complication of diabetes mellitus.Annals of medicine · 2026Review
- Grounded ophthalmic AI beyond the electronic health record trap: a critical narrative review.BMJ health & care informatics · 2026Review
- Article
- Ocular Factors Affecting AI Diagnosis in Diabetic Retinopathy Screening for Resource-Limited Regions: Cross-Sectional Study.Journal of medical Internet research · 2026Article
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Adaptive MDA-Net: Degradation-Aware Retinal Fundus Image Enhancement with Frequency-Domain and Illumination Guidance for Clinical Screening.Diagnostics (Basel, Switzerland) · 2026Article
- Clinical-guided deep learning framework for diabetic retinopathy: integrating lesion-aware attention, adversarial augmentation, and uncertainty quantification.Scientific reports · 2026Article
- Transfer Learning and Optimized Machine Learning Techniques for Multiclass Diabetic Retinopathy Classification Using Retinal Images.Diagnostics (Basel, Switzerland) · 2026Article
- Swin-DRNet: A robust transformer framework for diabetic retinopathy screening under heterogeneous imaging conditions.Scientific reports · 2026Article
- VMDiff: A novel hybrid CNN-Mamba generative framework for retinal phenotype image enhancement.Medical & biological engineering & computing · 2026Article
- MultiRetNet: A Lightweight Explainable AI Approach to Diabetic Retinopathy Grading and DME Detection Using Fundus-OCT Fusion.Journal of imaging · 2026Article
- RetCond: A Conditional Diffusion Model for Self-Explanatory Multi-Class Fundus Image Classification.Journal of medical systems · 2026Article
- A gated task-attentive multi-task network for unified retinal image analysis.Scientific reports · 2026Article
- Comparative Analysis of General-Purpose vs. Domain-Specific Multimodal Models for Diabetic Retinopathy Classification.Diagnostics (Basel, Switzerland) · 2026Article
90 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retinal screening contributes to early detection of diabetic retinopathy and timely treatment. To facilitate the screening process, we develop a deep learning system, named DeepDR, that can detect early-to-late stages of diabetic retinopathy. DeepDR is trained for real-time image quality assessment, lesion detection and grading using 466,247 fundus images from 121,342 patients with diabetes. Evaluation is performed on a local dataset with 200,136 fundus images from 52,004 patients and three external datasets with a total of 209,322 images. The area under the receiver operating characteristic curves for detecting microaneurysms, cotton-wool spots, hard exudates and hemorrhages are 0.901, 0.941, 0.954 and 0.967, respectively. The grading of diabetic retinopathy as mild, moderate, severe and proliferative achieves area under the curves of 0.943, 0.955, 0.960 and 0.972, respectively. In external validations, the area under the curves for grading range from 0.916 to 0.970, which further supports the system is efficient for diabetic retinopathy grading.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.