ArticleBMJ open ophthalmology2022
Deep learning-based classification of retinal vascular diseases using ultra-widefield colour fundus photographs.
Article in BMJ open ophthalmology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- Advances in AI-Driven EEG Analysis for Neurological and Oculomotor Disorders: A Systematic Review.Biosensors · 2025Pooled it
- Ultra-widefield color fundus photography in diabetic retinopathy: from panretinal assessment to multimodal integration.Frontiers in medicine · 2026Review
- Ultra-Widefield Retinal Imaging versus Dilated Fundus Examination for the Detection of Sickle Cell Retinopathy: A Systematic Review of Diagnostic Test Accuracy Studies.Clinical ophthalmology (Auckland, N.Z.) · 2026Review
- Artificial Intelligence Applications in Sickle Cell Retinopathy Imaging: Current Progress, Challenges, and Future Directions.Journal of ophthalmology · 2026Review
- Review
- Deep Learning to Distinguish Edema Secondary to Retinal Vein Occlusion and Diabetic Macular Edema: A Multimodal Approach Using OCT and Infrared Imaging.Journal of clinical medicine · 2025Article
- A comparative study of machine learning models for automated detection and classification of retinal diseases in Ghana.PloS one · 2025Article
- External Validation of Deep Learning Models for Classifying Etiology of Retinal Hemorrhage Using Diverse Fundus Photography Datasets.Bioengineering (Basel, Switzerland) · 2024Article
- Performance of automated machine learning in detecting fundus diseases based on ophthalmologic B-scan ultrasound images.BMJ open ophthalmology · 2024Article
- Comparing code-free and bespoke deep learning approaches in ophthalmology.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Review
- Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.Medicina (Kaunas, Lithuania) · 2024Review
- Algorithm of automatic identification of diabetic retinopathy foci based on ultra-widefield scanning laser ophthalmoscopy.International journal of ophthalmology · 2024Article
- Intelligent diagnosis of retinal vein occlusion based on color fundus photographs.International journal of ophthalmology · 2024Article
- Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus images.International journal of ophthalmology · 2024Review
- Article
- Discriminative-Region Multi-Label Classification of Ultra-Widefield Fundus Images.Bioengineering (Basel, Switzerland) · 2023Article
- Assessing retinal vein occlusion based on color fundus photographs using neural understanding network (NUN).Medical physics · 2023Article
- Research progress in artificial intelligence assisted diabetic retinopathy diagnosis.International journal of ophthalmology · 2023Article
- Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images.Frontiers in cell and developmental biology · 2023Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo assess the ability of a deep learning model to distinguish between diabetic retinopathy (DR), sickle cell retinopathy (SCR), retinal vein occlusions (RVOs) and healthy eyes using ultra-widefield colour fundus photography (UWF-CFP). METHODS AND ANALYSIS: In this retrospective study, UWF-CFP images of patients with retinal vascular disease (DR, RVO, and SCR) and healthy controls were included. The images were used to train a multilayer deep convolutional neural network to differentiate on UWF-CFP between different vascular diseases and healthy controls. A total of 224 UWF-CFP images were included, of which 169 images were of retinal vascular diseases and 55 were healthy controls. A cross-validation technique was used to ensure that every image from the dataset was tested once. Established augmentation techniques were applied to enhance performances, along with an Adam optimiser for training. The visualisation method was integrated gradient visualisation.
resultsThe best performance of the model was obtained using 10 epochs, with an overall accuracy of 88.4%. For DR, the area under the receiver operating characteristics (ROC) curve (AUC) was 90.5% and the accuracy was 85.2%. For RVO, the AUC was 91.2% and the accuracy 88.4%. For SCR, the AUC was 96.7% and the accuracy 93.8%. For healthy controls, the ROC was 88.5% with an accuracy that reached 86.2%.
conclusionDeep learning algorithms can classify several retinal vascular diseases on UWF-CPF with good accuracy. This technology may be a useful tool for telemedicine and areas with a shortage of ophthalmic care.
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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.