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. 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 assessed the vascular structure (PRCs)/function relationship at different glaucoma stages with Spearman's correlation. RESULTS: 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: r = 0.835, p < 0.001; non-NR: r = 0.871, p < 0.001) and VLD (NR: r = 0.829, p < 0.001; non-NR: r = 0.837, p < 0.001). For mild, moderate, and advanced glaucoma, the correlation coefficients between MD and VLD were 0.366 (p = 0.028) 0.081 (p = 0.689), and 0.427 (p = 0.017) with NR and 0.405 (p = 0.014), 0.184 (p = 0.360), and 0.339 (p = 0.062) without NR, respectively. CONCLUSION: 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.

Road Centerline Data Accumulation for Rescue Workers Whose Expertise Is Not GIS.

OBJECTIVES: The objective of this study is to provide road centerline data for professionals of disaster medicine areas who are often beginners in GIS use. METHODS: Newly developed vector tile format data were converted into shapefile format data, then were organized as second level medical districts to which medical professionals are accustomed. RESULTS: Road centerline data in Japan is being prepared to release from Association for Promotion of Infrastructure Geospatial Information Distribution free of charge. CONCLUSION: Professionals of disaster medicine areas increased their accessibility of GIS. Logistic planning for evacuation activities and dispatching of rescue teams were improved.

Color visual acuity in preperimetric glaucoma and open-angle glaucoma.

PURPOSE: To investigate the clinical significance of color visual acuity (CVA) in preperimetric glaucoma (PPG) and open-angle glaucoma (OAG). METHODS: A total of 123 eyes of 73 subjects (22 normal eyes, 14 PPG eyes, and 87 OAG eyes; mean age: 44.9 ± 10.1 years, age range: 21-64 years) were enrolled. CVA was tested for red, green-yellow, blue-green and blue-purple with a newly developed test. RESULTS: There was no statistical difference in clinical background factors, including age, sex, intraocular pressure, or spherical equivalent between the three groups. Red VA and blue-green VA were significantly worse in the OAG eyes than in the normal eyes (P = 0.008 and P = 0.015, respectively), although green-yellow VA and blue-purple VA were not significantly worse. Furthermore, red VA and blue-green VA were significantly correlated with MD in a group of eyes with either PPG or OAG (r = -0.23, P = 0.023; r = -0.25, P = 0.012, respectively), but green-yellow VA and blue-purple VA were not. CONCLUSION: Red VA and blue-green VA were detectably worse in eyes with OAG, in close association with the degree of functional loss. This suggests that measuring CVA with the new color test described here may be a promising supplement to existing methods of detecting glaucoma and evaluating its severity.