ArticleEye (London, England)2023
Artificial intelligence to distinguish retinal vein occlusion patients using color fundus photographs.
Article in Eye (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- The Underutilized Ocular Fundus in Emergency Departments: Current Progress and Future Prospect of Imaging and Artificial Intelligence for Patient Care.Ophthalmology science · 2026Review
- Evaluating an AI-assisted triage workflow for retinal diseases.PLOS digital health · 2026Article
- Evaluating Artificial Intelligence as a First-Pass Reader in Fundus Photograph Screening: A Multireader Workflow Validation Study.Diagnostics (Basel, Switzerland) · 2026Article
- Optimal Treatment of Retinal Vein Occlusion: An Updated Canadian Review and Recommendations.Clinical ophthalmology (Auckland, N.Z.) · 2026Review
- Screening cognitive impairment in patients with atrial fibrillation: A deep learning model based on retinal fundus photographs.Heart rhythm O2 · 2025Article
- Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea.BMC medical informatics and decision making · 2025Article
- A Future Picture: A Review of Current Generative Adversarial Neural Networks in Vitreoretinal Pathologies and Their Future Potentials.Biomedicines · 2025Review
- Elevated triglyceride-glucose index increases risk in patients with retinal vein occlusion.Frontiers in medicine · 2025Article
- Research progress in deep learning-based fundus image analysis for the diagnosis and prediction of hypertension-related diseases.Frontiers in cell and developmental biology · 2025Review
- Prediction of treatment outcome for branch retinal vein occlusion using convolutional neural network-based retinal fluorescein angiography.Scientific reports · 2024Article
- Retinal Vein Occlusion-Background Knowledge and Foreground Knowledge Prospects-A Review.Journal of clinical medicine · 2024Review
- Deep learning segmentation of non-perfusion area from color fundus images and AI-generated fluorescein angiography.Scientific reports · 2024Article
- Interpretable Detection of Diabetic Retinopathy, Retinal Vein Occlusion, Age-Related Macular Degeneration, and Other Fundus Conditions.Diagnostics (Basel, Switzerland) · 2024Article
- Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research.International journal of ophthalmology · 2024Review
- Advances in artificial intelligence models and algorithms in the field of optometry.Frontiers in cell and developmental biology · 2023Review
- Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images.Frontiers in cell and developmental biology · 2023Review
- Multimodal data-driven approaches in retinal vein occlusion: A narrative review integrating machine learning and bioinformatics.Advances in ophthalmology practice and researchReview
- Automated Detection of Central Retinal Artery Occlusion Using OCT Imaging via Explainable Deep Learning.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeOur aim is to establish an AI model for distinguishing color fundus photographs (CFP) of RVO patients from normal individuals.
methodsThe training dataset included 2013 CFP from fellow eyes of RVO patients and 8536 age- and gender-matched normal CFP. Model performance was assessed in two independent testing datasets. We evaluated the performance of the AI model using the area under the receiver operating characteristic curve (AUC), accuracy, precision, specificity, sensitivity, and confusion matrices. We further explained the probable clinical relevance of the AI by extracting and comparing features of the retinal images.
resultsOur model achieved an average AUC was 0.9866 (95% CI: 0.9805-0.9918), accuracy was 0.9534 (95% CI: 0.9421-0.9639), precision was 0.9123 (95% CI: 0.8784-9453), specificity was 0.9810 (95% CI: 0.9729-0.9884), and sensitivity was 0.8367 (95% CI: 0.7953-0.8756) for identifying fundus images of RVO patients in training dataset. In independent external datasets 1, the AUC of the RVO group was 0.8102 (95% CI: 0.7979-0.8226), the accuracy of 0.7752 (95% CI: 0.7633-0.7875), the precision of 0.7041 (95% CI: 0.6873-0.7211), specificity of 0.6499 (95% CI: 0.6305-0.6679) and sensitivity of 0.9124 (95% CI: 0.9004-0.9241) for RVO group. There were significant differences in retinal arteriovenous ratio, optic cup to optic disc ratio, and optic disc tilt angle (p = 0.001, p = 0.0001, and p = 0.0001, respectively) between the two groups in training dataset.
conclusionWe trained an AI model to classify color fundus photographs of RVO patients with stable performance both in internal and external datasets. This may be of great importance for risk prediction in patients with retinal venous occlusion.
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