PROJECT SUMMARY (See instructions): Open angle glaucoma (OAG) is a leading cause of irreversible blindness worldwide with significant disparities in prevalence, incidence, and progression depending on gender, race, and socio-economic status. OAG risk increases with age and presents not only an increasing economic burden on health care systems, but also a challenge in ensuring fair and equitable treatment approaches. Unfortunately, poor understanding of OAG risk factors has constrained currently approved treatments to intraocular pressure (IOP) reduction. Other factors such as vascular health, specifically blood pressure (BP), are known to alter risk of OAG onset and progression. BP and IOP vary by person, with both high and low BP being associated with the disease process. Thus, quantifying the relative contribution of BP as a risk factor in combination with IOP for a given individual inclusive of other demographic disparity factors will produce a translational framework to advance glaucoma management. The main goal of this project is to develop an innovative method for interpreting weighted contributions of IOP and BP, along with other risk factors such as age, gender and race. A significant outcome of this research will be a framework to assist clinicians in directing care to those who need it the most while preventing unnecessary treatments for those at lowest risk. The proposed method consists in: (i) developing a fast and accurate emulator for estimating the physiological status of a person's eye based on individual-specific values of BP and IOP (Smart Eye Emulator, SEE; Aim 1); (ii) developing a method for Transfer Learning of patient clusters across diverse and heterogeneous datasets that leverages the physiological-enhanced variables provided by SEE (physiology-enhanced data analytics; Aim 2); and (iii) ensuring relevance of the project outcomes by incorporating dynamically the feedback of end-users on the perceived usability and usefulness of the proposed approach in clinical practice (Aim 3). Our team includes experts in ophthalmology, mathematics, statistics, computer science, and health communication science who collaboratively are providing diverse multi-center datasets from multiple countries and continents. Ultimately, the outcomes of this project will provide new methods to quantify the relative weight of IOP and BP in the disease process of each individual and will identify characteristics that put people at-risk for