ArticleInternational journal of ophthalmology2021
Artificial intelligence can assist with diagnosing retinal vein occlusion.
Article in International journal of ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 36 citations in OpenAlex.
- Interpretability of multimodal neural networks for prediction of visual acuity in patients with branch retinal vein occlusion.Scientific reports · 2026Article
- RVO-ME: A Dual-Task OCT Dataset for Segmentation and Detection of Macular Lesions in Retinal Vein Occlusion.Scientific data · 2026Article
- Artificial intelligence-based diagnostic model construction and quantitative analysis of retinal vein occlusion lesions using color fundus photographs.Frontiers in cell and developmental biology · 2026Article
- Review
- A Future Picture: A Review of Current Generative Adversarial Neural Networks in Vitreoretinal Pathologies and Their Future Potentials.Biomedicines · 2025Review
- Deep learning detection of retinal detachment: Optical coherence tomography staging and estimation of duration of macular detachment.PloS one · 2025Article
- A Meta-Learning Approach for Classifying Multimodal Retinal Images of Retinal Vein Occlusion With Limited Data.Translational vision science & technology · 2024Article
- Prediction of treatment outcome for branch retinal vein occlusion using convolutional neural network-based retinal fluorescein angiography.Scientific reports · 2024Article
- Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review.Ophthalmology and therapy · 2024Review
- Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence.Journal of personalized medicine · 2024Review
- Artificial intelligence in chorioretinal pathology through fundoscopy: a comprehensive review.International journal of retina and vitreous · 2024Review
- Interpretable Detection of Diabetic Retinopathy, Retinal Vein Occlusion, Age-Related Macular Degeneration, and Other Fundus Conditions.Diagnostics (Basel, Switzerland) · 2024Article
- Intelligent diagnosis of retinal vein occlusion based on color fundus photographs.International journal of ophthalmology · 2024Article
- Artificial Intelligence and Infectious Keratitis: Where Are We Now?Life (Basel, Switzerland) · 2023Review
- A Datasheet for the INSIGHT University Hospitals Birmingham Retinal Vein Occlusion Data Set.Ophthalmology science · 2023Article
- Assessing retinal vein occlusion based on color fundus photographs using neural understanding network (NUN).Medical physics · 2023Article
- Analysis of retinal arteriolar and venular parameters in primary open angle glaucoma.International journal of ophthalmology · 2023Article
- Evaluation of a novel deep learning based screening system for pathologic myopia.International journal of ophthalmology · 2023Article
- Artificial intelligence-aided diagnosis and treatment in the field of optometry.International journal of ophthalmology · 2023Article
- Vault predicting after implantable collamer lens implantation using random forest network based on different features in ultrasound biomicroscopy images.International journal of ophthalmology · 2023Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo assist with retinal vein occlusion (RVO) screening, artificial intelligence (AI) methods based on deep learning (DL) have been developed to alleviate the pressure experienced by ophthalmologists and discover and treat RVO as early as possible.
methodsA total of 8600 color fundus photographs (CFPs) were included for training, validation, and testing of disease recognition models and lesion segmentation models. Four disease recognition and four lesion segmentation models were established and compared. Finally, one disease recognition model and one lesion segmentation model were selected as superior. Additionally, 224 CFPs from 130 patients were included as an external test set to determine the abilities of the two selected models.
resultsUsing the Inception-v3 model for disease identification, the mean sensitivity, specificity, and F1 for the three disease types and normal CFPs were 0.93, 0.99, and 0.95, respectively, and the mean area under the curve (AUC) was 0.99. Using the DeepLab-v3 model for lesion segmentation, the mean sensitivity, specificity, and F1 for four lesion types (abnormally dilated and tortuous blood vessels, cotton-wool spots, flame-shaped hemorrhages, and hard exudates) were 0.74, 0.97, and 0.83, respectively.
conclusionDL models show good performance when recognizing RVO and identifying lesions using CFPs. Because of the increasing number of RVO patients and increasing demand for trained ophthalmologists, DL models will be helpful for diagnosing RVO early in life and reducing vision impairment.
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