1. Deep learning-based noise reduction improves optical coherence tomography angiography imaging of radial peripapillary capillaries in advanced glaucoma

    Deep learning-based noise reduction improves optical coherence tomography angiography imaging of radial peripapillary capillaries in advanced glaucoma

    Purpose: We applied deep learning-based noise reduction (NR) to optical coherence tomography-angiography (OCTA) images of the radial peripapillary capillaries (RPCs) in eyes with glaucoma and investigated the usefulness of this method as an objective analysis of glaucoma. Subjects and methods: This cross-sectional study included 118 eyes of 94 open-angle glaucoma patients (male/female =38/56, age: 56.1 ± 10.3 years). We used OCTA (OCT-HS100, Canon) and built-in software (RX software, v. 4.5) to perform NR and calculate RPC vessel area density (VAD) and skeleton vessel length density (VLD). We also examined NR's effect on reproducibility. Finally, we ...

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