ArticleTranslational vision science & technology2021
Macular Ischemia Quantification Using Deep-Learning Denoised Optical Coherence Tomography Angiography in Branch Retinal Vein Occlusion.
Article in Translational vision science & technology, 2021. 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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5 citing papers in PubMed, 6 citations in OpenAlex.
- Review
- Ultra-Widefield Retinal Optical Coherence Tomography (OCT) and Angio-OCT Using an Add-On Lens.Diagnostics (Basel, Switzerland) · 2025Article
- Retinal capillary density among healthy Egyptian and South Asian students: an optical coherence tomography angiography study.International journal of ophthalmology · 2025Article
- Progress in clinical research and applications of retinal vessel quantification technology based on fundus imaging.Frontiers in bioengineering and biotechnology · 2024Review
- Convolutional Neural Network-Based Automated Quality Assessment of OCT and OCT Angiography Image Maps in Individuals With Neurodegenerative Disease.Translational vision science & technology · 2023Article
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
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Abstract
Purpose: To examine whether deep-learning denoised optical coherence tomography angiography (OCTA) images could enhance automated macular ischemia quantification in branch retinal vein occlusion (BRVO). Methods: This retrospective, single-center, cross-sectional study enrolled 74 patients with BRVO and 46 age-matched healthy subjects. The severity of macular ischemia was graded as mild, moderate, or severe. Denoised OCTA images were produced using a neural network model. Quantitative parameters derived from denoised images, including vessel density and nonperfusion area, were compared with those derived from the OCTA machine. The main outcome measures were correlations between quantitative parameters, and areas under receiver operating characteristic curves (AUCs) in classifying the severity of the macular ischemia. Results: The vessel density and nonperfusion area from denoised images were correlated strongly with the corresponding parameters from machine-derived images in control eyes and BRVO eyes with mild or moderate macular ischemia (all P < 0.001). However, no such correlation was found in eyes with severe macular ischemia. The vessel density and nonperfusion area from denoised images had significantly larger area under receiver operating characteristic curve than those derived from the original images in classifying moderate versus severe macular ischemia (0.927 vs 0.802 [P = 0.042] and 0.946 vs 0.797, [P = 0.022], respectively). There were no significant differences in the areas under receiver operating characteristic curve between the denoised images and the machine-derived parameters in classifying control versus BRVO, and mild versus moderate macular ischemia. Conclusions: A neural network model is useful for removing speckle noise on OCTA images and facilitating the automated grading of macular ischemia in eyes with BRVO. Translational Relevance: Deep-learning denoised optical coherence tomography angiography images could enhance automated macular ischemia quantification.
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