Predicting and Detecting Glaucoma Progression with New Imaging

Principal Investigator

Co-Principal Investigator

Summary

This project aims to examine whether state-of-the-art imaging techniques can accurately predict and detecting the worsening of glaucoma.

Injured retinal ganglion cells (RGCs) undergo ultrastructural changes that result in wavelength-dependent retinal reflectance changes before RGC loss occurs. Hyperspectral imaging is a powerful technique that could be used to detect wavelength-dependent changes in retinal reflectance. The first aim of this project thus seeks to examine whether spectral reflectance characteristics on hyperspectral imaging can be used to accurately predict the future rate of RGC loss. RGC loss results in characteristic arcuate patterns of neuroretinal tissue loss by the nature of the trajectories of the RGC axona.

Detailed Non-Technical Summary

This project will address the urgent need for more effective tools to predict and detect progression to prevent irreversible vision loss from glaucoma. It will, for the first time, examine whether hyperspectral imaging – a technique adapted from other fields (e.g. geology and ecology) – can be used to provide an in vivo marker of RGC injury that can predict disease progression. It will also examine whether high-resolution widefield OCT imaging can be used to exploit distinctive topographic patterns of progressive glaucomatous damage to enable detection of progression within a short timeframe.If successful, this project will overcome the current difficulty of identifying those at highest risk of glaucoma progression to target for more intensive initial treatments and close monitoring. It will also overcome the challenge of detecting progression within a short timeframe, thereby ensuring that appropriate modifications in treatments occur to prevent further irreversible vision loss as early as possible. Overcoming these obstacles will also expedite the discovery of new therapeutics (e.g., neuroprotective treatments or gene therapy) via improved identification of high-risk individuals. 

Publications

First published on: December 09, 2022

Last modified on: February 03, 2023