Glaucoma is the leading cause of irreversible blindness worldwide and is expected to affect more than 110 millionpeople worldwide within the next two decades. It is a degenerative disease that has a large impact both in termsof patient quality of life and in costs to the healthcare system. A critical need in glaucoma clinical managementand research is the ability to accurately identify patients likely to undergo rapid disease progression (i.e., losevisual function quickly). Currently, estimating the rate of progression for a patient requires several follow-up visitsover the course of multiple years. This delay in identifying progression leads to lost vision and increases the costof care. It also impacts clinical trials in glaucoma, increasing the time and cost needed to investigate noveltherapies for the disease. The goal of this Phase I STTR proposal is to use artificial intelligence techniques toimprove the accuracy and shorten the time for identifying raid progression in glaucoma. The primary outcome ofour Phase I proposal will enable an AI-based tool to identify rapid glaucomatous progression and will beimmediately ready for use in Phase 1/2a clinical trials as FDA approval is not required. Specifically, we will (1)use longitudinal optical coherence tomography (OCT) imaging and visual field (VF) testing dataset to train AImodels to identify rapidly progressing glaucoma patients and (2) incorporate patient data, clinical measurements,and treatment history into the AI models to further improve performance. AI models will be trained and evaluatedon a combination of research and real-world clinical data. These datasets include tens of thousands of images,VF tests, and clinical records collected from a diverse cohort of more than 9,000 glaucoma patients over thecourse of more than a decade. These datasets provide us with a unique opportunity to not only train AI models,but also to characterize model performance as a function of patient demographics, clinical covariates, diseaseseverity, and follow-up length - providing critical context to help clinicians better understand model predictions.Accurate and early predictions would be of great benefit to both clinical management and clinical trials inglaucoma. Improved outcomes, reduced patient care and drug development costs, and faster development ofglaucoma therapeutics make tools that quickly identify progressors an attractive product for our target customers,pharmaceutical companies and eye care specialists.
Public Health Relevance Statement: Project Narrative
Glaucoma is the leading cause of irreversible blindness worldwide and the inability to quickly identifying
glaucoma patients likely to undergo rapid disease progression is an ongoing problem that leads to lost vision
and an increased cost of care. Our goal is to combine artificial intelligence (AI) techniques to a large, longitudinal
dataset (9,000+ glaucoma patients, tens of thousands of clinical records) to develop and evaluate tools for
identifying rapid progression in glaucoma more quickly and more accurately. This predictive tool will be of great
benefit to both clinical management and clinical trials in glaucoma, with the potential to improve outcomes,
reduce patient care and drug development costs, and accelerate the pace of development for new glaucoma
therapeutics.
Project Terms:
