AI-Based Identification of Rapid Glaucoma Progression to Guide Clinical Management and Accelerate Clinical Trials

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Project Summary Glaucoma is the leading cause of irreversible blindness worldwide and is expected to affect more than 110 million people worldwide within the next two decades. It is a degenerative disease that has a large impact both in terms of patient quality of life and in costs to the healthcare system. A critical need in glaucoma clinical management and research is the ability to accurately identify patients likely to undergo rapid disease progression (i.e., lose visual function quickly). Currently, estimating the rate of progression for a patient requires several follow-up visits over the course of multiple years. This delay in identifying progression leads to lost vision and increases the cost of care. It also impacts clinical trials in glaucoma, increasing the time and cost needed to investigate novel therapies for the disease. The goal of this Phase I STTR proposal is to use artificial intelligence techniques to improve the accuracy and shorten the time for identifying raid progression in glaucoma. The primary outcome of our Phase I proposal will enable an AI-based tool to identify rapid glaucomatous progression and will be immediately 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 AI models 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 evaluated on 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 the course 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, disease severity, 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 in glaucoma. Improved outcomes, reduced patient care and drug development costs, and faster development of glaucoma therapeutics make tools that quickly identify progressors an attractive product for our target customers, pharmaceutical companies and eye care specialists.