1. Deep Learning-Based Noise Reduction Improves Optical Coherence Tomography Angiography Imaging of Radial Peripapillary Capillaries in Advanced Glaucoma
- Author
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Kazuko, Omodaka, Juun, Horie, Hikari, Tokairin, Chiho, Kato, Junko, Ouchi, Takahiro, Ninomiya, Sharma, Parmanand, Satoru, Tsuda, and Toru, Nakazawa
- Subjects
Male ,Optic Disk ,Angiography ,Retinal Vessels ,Reproducibility of Results ,Glaucoma ,Middle Aged ,Sensory Systems ,Capillaries ,Cellular and Molecular Neuroscience ,Ophthalmology ,Cross-Sectional Studies ,Deep Learning ,Humans ,Female ,Visual Fields ,Fluorescein Angiography ,Tomography, Optical Coherence ,Glaucoma, Open-Angle ,Intraocular Pressure ,Aged - Abstract
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.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 assessed the vascular structure (PRCs)/function relationship at different glaucoma stages with Spearman's correlation.Regardless of NR, RPC parameters had excellent coefficients of variation (1.7-4.1%) in glaucoma patients and controls, and mean deviation (MD) was significantly correlated with VAD (NR:Denoised RPC images might have the potential for a closer structural/functional relationship, in which the floor effect of retinal nerve fiber layer thickness affects measurements. Deep learning-based NR promises to improve glaucoma assessment.
- Published
- 2022
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