# Predicting the risk of glaucoma from structural, functional, and genetic factors using artificial intelligence

> **NIH NIH R01** · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · 2022 · $573,819

## Abstract

Glaucoma is a complex neurodegenerative blinding disease that causes the degeneration of retinal ganglion
cells and their axons. The prevalence of glaucoma is projected to increase by almost 50% over the next two
decades as older people making up the fastest growing part of the global population. The burden of glaucoma
care will therefore continue to grow, without a competing increase in the number of ophthalmologists or
available resources. As a result, the required demand for glaucoma care will likely exceed capacity and
resources leading to prioritizing care for those patients at highest risk of vision loss. There is no concrete
evidence in support of an individual test, or group of tests, that show superiority for identifying people at-risk of
developing glaucoma or those at higher risk of glaucoma progression. Glaucoma risk factors are too
insensitive in identifying individuals who will likely develop glaucoma. Fundus photographs lack detailed and
high-resolution information of the optic disc and surrounding retinal nerve fiber layer for glaucoma assessment
and visual field tests provide surprisingly inconsistent and variable results, especially in subclinical glaucoma
and in patients with more severe visual field loss (both sides of glaucoma spectrum). Although glaucoma is a
highly inheritable disease, genetic factors yet explain only slight segment of all glaucoma. Reliable and
accurate models for detecting individuals at higher risk of visual loss is an unmet need. We propose to use
artificial intelligence (AI) constructs to discover visual field and imaging signatures of glaucoma and synthesize
these signatures with classic risk factors and genetic data to identify individuals at-risk of developing glaucoma
and future vision loss. The central hypothesis of this proposal is that AI applied to fundus photographs, visual
fields and genetic factors may recognize and quantify the glaucoma-induced signs, yielding better signatures
for glaucoma development and vision loss compared to current subjectively specified or conventionally
identified features. As such, we will develop AI models to predict glaucoma from fundus photographs and
visual fields then extract fundus and visual field endophenotypes (signatures) of glaucoma. We will then
develop genome-wide association study (GWAS) and machine learning models to address underpower GWAS
limitation and develop AI models to predict glaucoma from identified genetic markers. We finally develop an AI
construct to synthesizes the discovered fundus and visual field signatures with classic glaucoma risk factors
and genetic data to predict glaucoma. This AI construct can work with any or all of these modalities as well
thus providing a potential tool for screening purposes as well. To achieve these objectives, we have assembled
a team of interdisciplinary experts with access to large clinically annotated multi-modal glaucoma data.
Our proposed studies will potentially uncover novel genetic factors of glau...

## Key facts

- **NIH application ID:** 10364871
- **Project number:** 1R01EY033005-01A1
- **Recipient organization:** UNIVERSITY OF TENNESSEE HEALTH SCI CTR
- **Principal Investigator:** Siamak Yousefi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $573,819
- **Award type:** 1
- **Project period:** 2022-04-01 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10364871

## Citation

> US National Institutes of Health, RePORTER application 10364871, Predicting the risk of glaucoma from structural, functional, and genetic factors using artificial intelligence (1R01EY033005-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10364871. Licensed CC0.

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