Harnessing Artificial Intelligence to Identify Risk Genes for Glaucoma
Co-Principal InvestigatorsStuart MacGregor, PhD
We aim to use innovative approaches harnessing artificial intelligence to grade eye nerve damage and identify genes causing those damages over time, leading to glaucoma. Aim 1 - Use artificial intelligence (AI) to measure eye nerve damage accurately and predict retinal thinning (which are indicators of glaucoma development) from simple eye images. Aim 2 - Identify genetic variants contributing to eye nerve damage and glaucoma development and progression using AI data from Aim 1. This aim will also leverage AI to identify which genes cause eye nerve damage, subsequently informing the development of potential new treatments for glaucoma by targeting those risk genes, which may protect from (or reverse) eye nerve damage.
This study will be among the first large-scale genetic studies to use artificial intelligence (AI) to accurately grade glaucoma-related damages of the eye nerve and retina at the back of the eye. In addition, this is the first study to investigate the genetic basis of eye nerve damage over time using AI approaches. Finally, current treatments for glaucoma are not capable of preventing blindness once the eye nerve is already injured. This study may provide new potential candidate treatments for glaucoma, which are beneficial even once the eye nerve is damaged. This study provides a novel and time-efficient approach using artificial intelligence to grade eye nerve damage, which will improve the accuracy of risk gene discovery for glaucoma. These genes will subsequently be used to develop a genetic test to identify people at high risk of developing eye nerve damage and glaucoma, providing unique opportunities to prevent blindness in those people. Identifying causal genes leads to a better understanding of the underlying biology of glaucoma and may provide drug candidates allowing for the treatment of the damaged eye nerve, which currently is not possible.