Using Artificial Intelligence to Discover Genetic Risk Factors for Glaucoma Worsening

Using machine learning to extract richer measures of glaucoma worsening from imaging and visual field data, this project aims to identify the genes and biological pathways that drive glaucoma progression

Glaucoma is a leading cause of blindness worldwide, yet physicians cannot predict who will lose vision quickly. This project studies the role of genetics in glaucoma progression using clinical data and machine learning methods. Discoveries may allow earlier detection of high-risk patients, more personalized treatments, and new targets to slow or prevent vision loss.

Most genetic studies of glaucoma have focused on who gets the disease, not on why some patients lose vision rapidly while others remain stable for years. This proposal is among the first to use artificial intelligence to extract richer, more precise measures of glaucoma worsening from eye scans and visual tests, and then use those measures to search for the responsible genes. By combining cutting-edge AI with large datasets from thousands of patients, we have a unique opportunity to discover the genetic factors that drive vision loss in glaucoma — opening the door to earlier intervention and personalized treatment.

If successful, this research will identify the genetic factors that cause glaucoma to worsen, enabling doctors to predict which patients are at highest risk of rapid vision loss and tailor treatment accordingly before irreversible damage occurs. For the research field, the AI-derived progression measures developed here will serve as powerful new tools for future genetic studies, clinical trials, and drug development. Ultimately, this work lays the foundation for personalized glaucoma care, where treatment intensity is guided by a patient’s individual genetic risk profile rather than a one-size-fits-all approach.