ArticleEye (London, England)2022
Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs.
Article in Eye (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 147 citations in OpenAlex.
- Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis.Frontiers in endocrinology · 2025Pooled it
- In-depth analysis of research hotspots and emerging trends in AI for retinal diseases over the past decade.Frontiers in medicine · 2024Pooled it
- Deep learning-based postoperative visual acuity prediction in idiopathic epiretinal membrane.BMC ophthalmology · 2023Trial
- MultiRetNet: A Lightweight Explainable AI Approach to Diabetic Retinopathy Grading and DME Detection Using Fundus-OCT Fusion.Journal of imaging · 2026Article
- Diabetic retinopathy severity detection using an improved Whale optimization algorithm and convolutional Kolmogorov-Arnold network.Frontiers in medicine · 2026Article
- Deep learning-based joint analysis of diabetic retinopathy and glaucoma in retinal fundus images.Scientific reports · 2025Article
- Current status and solutions for AI ethics in ophthalmology: a bibliometric analysis.NPJ digital medicine · 2025Article
- Attention dual transformer with adaptive temporal convolutional for diabetic retinopathy detection.Scientific reports · 2025Article
- A deep learning based model for diabetic retinopathy grading.Scientific reports · 2025Article
- The application of artificial intelligence-generated content in ophthalmology education.Frontiers in medicine · 2025Review
- FedCMC: a federated learning model with contribution fairness based on multi-center core data extraction for assessing the myometrial invasion status of endometrial cancer.Frontiers in oncology · 2025Article
- Semi-supervised 3D retinal fluid segmentation via correlation mutual learning with global reasoning attention.Biomedical optics express · 2024Article
- Domain Adaptation-Based Deep Learning Model for Forecasting and Diagnosis of Glaucoma Disease.Biomedical signal processing and control · 2024Article
- RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis.NPJ digital medicine · 2024Article
- Ensemble of deep convolutional neural networks is more accurate and reliable than board-certified ophthalmologists at detecting multiple diseases in retinal fundus photographs.The British journal of ophthalmology · 2024Article
- Implementing and evaluating a fully functional AI-enabled model for chronic eye disease screening in a real clinical environment.BMC ophthalmology · 2024Article
- URNet: System for recommending referrals for community screening of diabetic retinopathy based on deep learning.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Multiple instance learning based classification of diabetic retinopathy in weakly-labeled widefield OCTA en face images.Scientific reports · 2023Article
- Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Research progress in artificial intelligence assisted diabetic retinopathy diagnosis.International journal of ophthalmology · 2023Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo present and validate a deep ensemble algorithm to detect diabetic retinopathy (DR) and diabetic macular oedema (DMO) using retinal fundus images.
methodsA total of 8739 retinal fundus images were collected from a retrospective cohort of 3285 patients. For detecting DR and DMO, a multiple improved Inception-v4 ensembling approach was developed. We measured the algorithm's performance and made a comparison with that of human experts on our primary dataset, while its generalization was assessed on the publicly available Messidor-2 dataset. Also, we investigated systematically the impact of the size and number of input images used in training on model's performance, respectively. Further, the time budget of training/inference versus model performance was analyzed.
resultsOn our primary test dataset, the model achieved an 0.992 (95% CI, 0.989-0.995) AUC corresponding to 0.925 (95% CI, 0.916-0.936) sensitivity and 0.961 (95% CI, 0.950-0.972) specificity for referable DR, while the sensitivity and specificity for ophthalmologists ranged from 0.845 to 0.936, and from 0.912 to 0.971, respectively. For referable DMO, our model generated an AUC of 0.994 (95% CI, 0.992-0.996) with a 0.930 (95% CI, 0.919-0.941) sensitivity and 0.971 (95% CI, 0.965-0.978) specificity, whereas ophthalmologists obtained sensitivities ranging between 0.852 and 0.946, and specificities ranging between 0.926 and 0.985.
conclusionThis study showed that the deep ensemble model exhibited excellent performance in detecting DR and DMO, and had good robustness and generalization, which could potentially help support and expand DR/DMO screening programs.
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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.