# SCH: SEEthroughGLAUCOMA: Smart Eye Emulator (SEE) to study glaucoma risk factors

> **NIH NIH R01** · UNIVERSITY OF MAINE ORONO · 2024 · $225,049

## Abstract

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

## Key facts

- **NIH application ID:** 10930934
- **Project number:** 5R01EY034718-04
- **Recipient organization:** UNIVERSITY OF MAINE ORONO
- **Principal Investigator:** Giovanna Guidoboni
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $225,049
- **Award type:** 5
- **Project period:** 2022-09-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10930934

## Citation

> US National Institutes of Health, RePORTER application 10930934, SCH: SEEthroughGLAUCOMA: Smart Eye Emulator (SEE) to study glaucoma risk factors (5R01EY034718-04). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10930934. Licensed CC0.

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