# Improved Glaucoma Monitoring Using Artificial-Intelligence Enabled Dashboard

> **NIH NIH R21** · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · 2021 · $191,826

## Abstract

Detecting functional and structural loss due to glaucoma is critical to making treatment decisions with the goal
of preserving vision and maintaining quality of life. However, most of the approaches for glaucoma assessment
through visual fields (VFs) or optical coherence tomography (OCT) measurements have several limitations that
poses critical challenge to their clinical utility.
Identifying glaucoma-induced changes from a sequence of VF or OCT data is challenging either if the patients
is in the early stages of the disease with subtle manifested structural and functional signs or if the patients are
in the later stages of the disease with significant VF variability and OCT flooring effect. A major limitation of
the current glaucoma monitoring techniques is that they generate a binary outcome of whether the glaucoma is
worsening or not while current high-throughput data (e.g., OCT) has more information than a binary outcome.
Another major drawback of some of these approaches is that they rely on traditional paradigms for progression
detection such as linear regression. However, rates of glaucomatous progression may be non-linear and rapid,
particularly during the later stages of the disease. Another limitation is that ad-hoc rules are adopted to define
glaucoma progression while objective criteria are required to define thresholds for progression. Finally, a major
deficiency of most of these methods is that they lack advanced visualization and interpretation.
We propose to address these limitations by developing artificial intelligence (AI)-enabled visualization tools for
effectively monitoring the functional and structural loss in patients with glaucoma. This approach provides
qualitative and quantitative means to monitor 1) global visual functional and structural worsening, 2) extent of
loss in hemifields, and 3) local patterns of functional and structural loss on advanced 2-D visualization tools. To
achieve these objectives, we have assembled a team of interdisciplinary experts with access to large clinically
annotated glaucoma data.
The central hypothesis of this proposal is that advanced interpretable machine learning applied to a complete
profile of VFs in all test locations (e.g., 54 in 24-2 system) and OCT-derived measurements of retinal nerve
fiber layer (RNFL) (e.g., 768 A-scans around the optic disc and 7 global sectoral regions) can objectively and
automatically learn and quantify the most important features, yielding a more specific and sensitive means for
monitoring of glaucoma worsening than current subjectively-specified or statistically-identified approaches.
We also hypothesize that machine learning can provide interpretable models with several layers of glaucoma
knowledge that may provide a promising complement to current glaucoma assessment tests.
Our proposed studies may offer substantial improvements in prognosis and management of glaucoma through
effective use of analysis and visualization to improve glaucoma management an...

## Key facts

- **NIH application ID:** 10242048
- **Project number:** 5R21EY031725-02
- **Recipient organization:** UNIVERSITY OF TENNESSEE HEALTH SCI CTR
- **Principal Investigator:** Siamak Yousefi
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $191,826
- **Award type:** 5
- **Project period:** 2020-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10242048

## Citation

> US National Institutes of Health, RePORTER application 10242048, Improved Glaucoma Monitoring Using Artificial-Intelligence Enabled Dashboard (5R21EY031725-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10242048. Licensed CC0.

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