1. Macular Ischemia Quantification Using Deep-Learning Denoised Optical Coherence Tomography Angiography in Branch Retinal Vein Occlusion

    Macular Ischemia Quantification Using Deep-Learning Denoised Optical Coherence Tomography Angiography in Branch Retinal Vein Occlusion

    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 ...

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