# Novel Glaucoma Diagnostics for Structure and Function.

> **NIH NIH R01** · WILLS EYE HEALTH SYSTEM · 2024 · $1,308,371

## Abstract

Project Summary
Glaucoma is a leading cause of vision morbidity and blindness worldwide. Early disease detection, sensitive
monitoring of progression and prediction of future glaucoma progression are crucial to allow timely and
personalized treatment for preservation of vision. The introduction of ocular imaging technologies significantly
improves these capabilities, but in clinical practice there are still substantial challenges. At certain stages of the
disease severity spectrum, particularly in the early stage and in advanced disease, there are a variety of issues
that must be confronted that change over the course of the disease, including large between-subject variability,
inherent measurement variability, image quality, varying dynamic ranges of measurements, minimal
measurable level of tissues, etc. In addition, differences between optical coherence tomography (OCT) devices
cause difficulties in clinical patient care as scan data are not interchangeable. This is due to differences in
signal and image acquisition and processing, as well as the proprietary nature of the device software. In this
proposal, we build on our long-standing contribution to ocular imaging and propose novel and sensitive means
to detect glaucoma and identify and predict progression that are optimized to the various stages of disease
severity. We will use advanced signal and image processing and image analysis techniques for OCT, a leading
ocular imaging technology, to improve and harmonize images agnostic to OCT acquisition device.
Commonly used parameters provided by the technologies and newly developed parameters found to have
good diagnostic potential will be analyzed across the entire disease severity spectrum to identify optimal
metrics for each stage of the disease. We will use state-of-the-art machine learning methods, including deep
learning and diffusion modelling analysis approaches, to identify structural features embedded within OCT
images that are associated with glaucoma and its progression without any a priori assumptions. This will
provide novel insight into structural information and has shown very encouraging results to date. We will use
recently developed analytical techniques including multidimensional information compression analysis
(MICA) utilizing cluster structure function, a compression-based criterion function for optimizing visualization
and clustering via low-dimensional embeddings of the OCT images as well as enhanced machine and deep
learning models incorporating clinical features with OCT and visual fields to significantly improve
glaucoma detection and the identification of progression and prediction of future disease progression. This
program will advance the use of structural, functional, and other clinical information to produce a significant
impact on the clinical management of subjects with glaucoma.

## Key facts

- **NIH application ID:** 10979405
- **Project number:** 2R01EY013178-25
- **Recipient organization:** WILLS EYE HEALTH SYSTEM
- **Principal Investigator:** Joel S Schuman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,308,371
- **Award type:** 2
- **Project period:** 2000-08-01 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10979405

## Citation

> US National Institutes of Health, RePORTER application 10979405, Novel Glaucoma Diagnostics for Structure and Function. (2R01EY013178-25). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10979405. Licensed CC0.

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