ArticleThe British journal of ophthalmology2024
Ensemble of deep convolutional neural networks is more accurate and reliable than board-certified ophthalmologists at detecting multiple diseases in retinal fundus photographs.
Article in The British journal of ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 28 citations in OpenAlex.
- Performance of Deep Learning in Classifying Age-Related Macular Degeneration From Images: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- Diagnostic accuracy of deep learning using ultra-widefield fundus imaging for retinal detachment: a systematic review and meta-analysis.BMC ophthalmology · 2026Pooled it
- Article
- Early retinal disease detection from fundus images using deep neural networks.Scientific reports · 2026Article
- An Ensemble Learning Artificial Intelligence Model for Alzheimer's Disease Detection Using OCT.Ophthalmology science · 2026Article
- Intelligent retinal disease detection using deep learning.Scientific reports · 2025Article
- ODDM: Integration of SMOTE Tomek with Deep Learning on Imbalanced Color Fundus Images for Classification of Several Ocular Diseases.Journal of imaging · 2025Article
- A Lightweight CNN for Multiclass Retinal Disease Screening with Explainable AI.Journal of imaging · 2025Article
- Enhancing medical students' diagnostic accuracy of infectious keratitis with AI-generated images.BMC medical education · 2025Article
- Detecting Glaucoma in Highly Myopic Eyes From Fundus Photographs Using Deep Convolutional Neural Networks.Clinical & experimental ophthalmology · 2025Article
- Realistic fundus photograph generation for improving automated disease classification.The British journal of ophthalmology · 2025Article
- Comparing Auto-Machine Learning and Expert-Designed Models in Diagnosing Vitreomacular Interface Disorders.Journal of clinical medicine · 2025Article
- Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence.Medicina (Kaunas, Lithuania) · 2025Review
- A dual attention and multi-scale fusion network for diabetic retinopathy image analysis.Frontiers in medicine · 2025Article
- Article
- Insights into the liver-eyes connections, from epidemiological, mechanical studies to clinical translation.Journal of translational medicine · 2023Review
- OpthaNet: Attention-Integrated Architecture for High-Precision Multi-Class Ophthalmic Image Classification.Healthcare technology lettersArticle
- Fundus image classification using feature concatenation for early diagnosis of retinal disease.Digital healthArticle
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsTo develop an algorithm to classify multiple retinal pathologies accurately and reliably from fundus photographs and to validate its performance against human experts.
methodsWe trained a deep convolutional ensemble (DCE), an ensemble of five convolutional neural networks (CNNs), to classify retinal fundus photographs into diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and normal eyes. The CNN architecture was based on the InceptionV3 model, and initial weights were pretrained on the ImageNet dataset. We used 43 055 fundus images from 12 public datasets. Five trained ensembles were then tested on an 'unseen' set of 100 images. Seven board-certified ophthalmologists were asked to classify these test images.
resultsBoard-certified ophthalmologists achieved a mean accuracy of 72.7% over all classes, while the DCE achieved a mean accuracy of 79.2% (p=0.03). The DCE had a statistically significant higher mean F1-score for DR classification compared with the ophthalmologists (76.8% vs 57.5%; p=0.01) and greater but statistically non-significant mean F1-scores for glaucoma (83.9% vs 75.7%; p=0.10), AMD (85.9% vs 85.2%; p=0.69) and normal eyes (73.0% vs 70.5%; p=0.39). The DCE had a greater mean agreement between accuracy and confident of 81.6% vs 70.3% (p<0.001). DISCUSSION: We developed a deep learning model and found that it could more accurately and reliably classify four categories of fundus images compared with board-certified ophthalmologists. This work provides proof-of-principle that an algorithm is capable of accurate and reliable recognition of multiple retinal diseases using only fundus photographs.
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