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Vincent Khou, Michael Kalloniatis, Janelle Tong, Matt Trinh, David Alonso-Caneiro, Barbara Zangerl; Development of a High-Density Spatially Localized Model of the Human Retina. Invest. Ophthalmol. Vis. Sci. 2020;61(7):497.
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© ARVO (1962-2015); The Authors (2016-present)
Cluster analysis has been previously utilized to identify retinal changes with age from optical coherence tomography (OCT) data. However, these studies have mostly relied upon the existing 8 × 8 square grid (each grid measuring 860 × 860 µm in size) found in commercially available software. We utilized customized higher density grid sampling to characterize senescent thickness changes in the central retinal layers.
Spectralis OCT macular scans were obtained for two hundred and fifty-three single eyes segmented into total macula (TM), retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), and photoreceptor layer (PRL, defined as external limiting membrane to retinal pigment epithelium). Layer thicknesses were extracted from a 60 × 60 grid, each grid measuring 114.7 × 114.7 µm in size, using MATLAB (version R2019a, Mathworks, Natick, MA). Hierarchical cluster analysis was performed to identify separable classes with similar thicknesses as a function of age. Quadratic and linear regression analysis were applied thereafter to characterize age-related change.
Hierarchical cluster analysis identified classes representing statistically similar areas characterized as a function of age. 13 classes were identified for TM (depicted in pseudocolor in Figure 1), 18 for RNFL, 19 for GCL, 16 for IPL, 15 for INL, 9 for OPL, 16 for ONL, and 10 for PRL. Quadratic regression analysis was superior to linear regression to fit senescent changes for all classes of TM and GCL (F-test, P <0.001–0.0388), while no preference was identified for other retinal layers. Rates of decline were dependent on eccentricity.
We provide a high resolution spatially localized model of retinal thickness of total retina and segmented retinal layers as a function of age. The thickness density changes resemble known topographic changes from human anatomy. The model provides insights for anatomical areas where understanding of senescence is currently limited and has the potential to provide a high-density structural map to examine changes secondary to localized or global retinal disease.
This is a 2020 ARVO Annual Meeting abstract.
Figure 1. Pseudocolor clusters of TM derived from the Spectralis OCT macular scan. Each pseudocolor reflects statistically distinct classes, identifying areas of similar total macular retinal thickness changes as a function of age.
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