# Early Detection of Progressive Visual Loss in Glaucoma Using Deep Learning

> **NIH VA I01** · IOWA CITY VA MEDICAL CENTER · 2022 · —

## Abstract

Glaucoma, a leading cause of irreversible blindness, disproportionately affects veterans. While often progressing
slowly, glaucoma can also progress rapidly, and especially given the variability of standard visual-field (VF) tests
to monitor progression, it currently can be challenging to determine those individuals needing a more aggressive
treatment plan. Veterans may experience permanent loss of vision (and corresponding vision-related quality of
life) while waiting for subsequent tests to show VF loss progression (and thus indicating a change in treatment
is needed). Structural optical coherence tomography (OCT) measures, such as the thickness of the macular
ganglion cell layer (GCL), retinal nerve fiber layer (RNFL) and optic disc morphology can also be used to help
monitor progression. However, existing clinical use of global parameters to assess glaucoma progression may
be insensitive to worsening of focal defects. It is also not known how differing spatial patterns of progression
affects quality of life. There is an unmet clinical need for simple-to-use approaches to more accurately estimate
future progression and corresponding quality-of-life measures. We will use a specific type of deep-learning
approach, called deep variational autoencoders (VAEs) to provide a novel standardized and sensitive approach
to monitoring glaucomatous progression, comparable to a glaucoma expert. Our specific aims are as follows:
 1. Evaluate how well image-based deep-learning variational autoencoder (VAE) models can be used
 to monitor a patient’s current glaucomatous progression. This aim will first involve training and
 evaluating a separate deep VAE model for each image-based structure of interest as well as a deep VAE
 model for 24-2 visual field threshold data. Once trained, each VAE model will allow for the extraction of the
 so-called latent variable values given the input image. The ability of these latent variable values to monitor
 change over time will be compared (in an independent test set) to standard global and regional parameters.
 Because of their ability to naturally capture both global and local changes, the latent-variable approach will
 be able to better detect changes over time compared to current clinical reports.
2. Evaluate how well image-based deep-learning variational autoencoder (VAE) models can be used
 to predict a patient’s future glaucomatous progression. In this aim, we will first develop an approach
 for predicting future latent-variable representations of structure/function based on learning from a prior time
 series of values. Once determined, future latent values will be mapped back to their original
 structure/function representations using the trained “decoder” part of the VAE. Such an approach will
 provide a clear advantage for a clinician in having visual spatial representations of future structure and
 function trajectories to optimize early treatment decisions.
3. Evaluate how latent variables from a novel binocula...

## Key facts

- **NIH application ID:** 10424899
- **Project number:** 1I01RX003797-01A1
- **Recipient organization:** IOWA CITY VA MEDICAL CENTER
- **Principal Investigator:** MONA K. GARVIN
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2022
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2022-06-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10424899

## Citation

> US National Institutes of Health, RePORTER application 10424899, Early Detection of Progressive Visual Loss in Glaucoma Using Deep Learning (1I01RX003797-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10424899. Licensed CC0.

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