# A hybrid artificial intelligence framework for glaucoma monitoring

> **NIH NIH R21** · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · 2020 · $220,808

## Abstract

Glaucoma is a complex neurodegenerative disease that results in degeneration of retinal ganglion cells and
their axons. With older people making up the fastest growing part of the US population, glaucoma will become
even more prevalent in the US in the coming decades. Due to the complex interaction of multiple factors in
glaucoma, better structural and functional predictors are needed for its progression. The main impediments
are massive health record data and sophisticated computational models. Our overall goal is to leverage the
power of big data and rapidly evolving machine learning approaches. The NEI's “Big Data to Knowledge
(BD2K)” initiative and the American Academy of Ophthalmology Intelligent Research in Sight (IRIS) registry
are all efforts to exploit the power of data and to better understand diseases and to provide improved
prevention and treatment.
 In this multi-PI proposal, we offer to assemble over 1 million optical coherence tomography (OCT) and
visual fields (VFs) from the glaucoma research network (GRN). We propose to develop a hybrid artificial
intelligence (AI) algorithm that synthesizes Gaussian mixture model expectation maximization (GEM) and
archetypal machine learning approach to identify glaucoma progression and its monitoring using VFs and
retinal nerve fiber layer (RNFL) thickness measurements. We will make these tools openly available to the
vision and ophthalmology research communities.
 Our proposed studies could offer substantial improvements in the prognosis of glaucoma as well as
potentially providing OCT and joint VF/OCT surrogate endpoints to be used in glaucoma clinical trials.

## Key facts

- **NIH application ID:** 9892013
- **Project number:** 5R21EY030142-02
- **Recipient organization:** UNIVERSITY OF TENNESSEE HEALTH SCI CTR
- **Principal Investigator:** Tobias Elze
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $220,808
- **Award type:** 5
- **Project period:** 2019-04-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9892013

## Citation

> US National Institutes of Health, RePORTER application 9892013, A hybrid artificial intelligence framework for glaucoma monitoring (5R21EY030142-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9892013. Licensed CC0.

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