ArticleMedical physics2023
Assessing retinal vein occlusion based on color fundus photographs using neural understanding network (NUN).
Article in Medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- 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
- Optimal Treatment of Retinal Vein Occlusion: An Updated Canadian Review and Recommendations.Clinical ophthalmology (Auckland, N.Z.) · 2026Review
- Review
- Classification of Color Fundus Photographs Using Fusion Extracted Features and Customized CNN Models.Healthcare (Basel, Switzerland) · 2023Article
- CT-derived body composition associated with lung cancer recurrence after surgery.Lung cancer (Amsterdam, Netherlands) · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
objectiveTo develop and validate a novel deep learning architecture to classify retinal vein occlusion (RVO) on color fundus photographs (CFPs) and reveal the image features contributing to the classification.
methodsThe neural understanding network (NUN) is formed by two components: (1) convolutional neural network (CNN)-based feature extraction and (2) graph neural networks (GNN)-based feature understanding. The CNN-based image features were transformed into a graph representation to encode and visualize long-range feature interactions to identify the image regions that significantly contributed to the classification decision. A total of 7062 CFPs were classified into three categories: (1) no vein occlusion ("normal"), (2) central RVO, and (3) branch RVO. The area under the receiver operative characteristic (ROC) curve (AUC) was used as the metric to assess the performance of the trained classification models.
resultsThe AUC, accuracy, sensitivity, and specificity for NUN to classify CFPs as normal, central occlusion, or branch occlusion were 0.975 (± 0.003), 0.911 (± 0.007), 0.983 (± 0.010), and 0.803 (± 0.005), respectively, which outperformed available classical CNN models.
conclusionThe NUN architecture can provide a better classification performance and a straightforward visualization of the results compared to CNNs.
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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.