# Objective Quantification of Neural Damage for Screening, Diagnosis and Monitoring of Glaucoma with Fundus Photographs

> **NIH NIH R21** · DUKE UNIVERSITY · 2021 · $195,212

## Abstract

PROJECT SUMMARY
Glaucoma is a progressive optic neuropathy and the leading cause of irreversible blindness in the world. As the
disease remains largely asymptomatic until late stages, there is a pressing need to develop affordable
approaches for screening before visual impairment occurs. Although sophisticated imaging technologies such
as Spectral domain-optical coherence tomography (SDOCT) can provide highly reproducible and accurate
quantitative assessment of glaucomatous damage, their application in widespread screening or non-specialized
settings is unfeasible, given the high cost and operator requirements. Fundus photography is a low-cost
alternative that has been used successfully in teleophthalmology programs. However, subjective human grading
of fundus photos for glaucoma is poorly reproducible and highly inaccurate, as gradings tend to largely over- or
underestimate damage. We propose a new paradigm for assessing glaucomatous damage by training a deep
learning (DL) convolutional neural network to provide quantitative estimates of the amount of neural damage
from fundus photographs. In our Machine-to-Machine (M2M) approach, we trained a DL network to analyze
fundus photos and predict quantitative measurements of glaucomatous damage provided by SDOCT, such as
retinal nerve fiber layer (RNFL) thickness and neuroretinal rim measurements. Our preliminary results showed
that the M2M predictions have very high correlation and agreement with the original SDOCT observations. This
provides an objective method to quantify neural damage in fundus photos without requiring human graders,
which could potentially be used for screening, diagnoses and monitoring in teleophthalmology and non-
specialized point-of-care settings. In this proposal, we aim at refining and validating the M2M model in suitable,
large datasets from population-based studies, electronic medical records, and clinical trial data. Our central
hypothesis is that the M2M approach will be more accurate than subjective human gradings in screening,
diagnosing, predicting and detecting longitudinal damage over time. In Aim 1, we will investigate the performance
of the M2M model to screen for glaucomatous damage using large datasets from 6 population-based studies:
Blue Mountains Eye Study, Los Angeles Latino Eye Study, Tema Eye Survey, Beijing Eye Study, Central India
Eye and Medical Study and the Ural Eye and Medical Study, which will provide data on over 25,000 subjects of
diverse racial groups. In Aim 2, we will investigate the ability of the M2M model to predict future development of
glaucoma in eyes of suspects using the data from the Ocular Hypertension Treatment Study (OHTS). In Aim 3,
we will investigate the ability of the M2M model in detecting glaucomatous progression over time using data from
the Duke Glaucoma Registry, a large database of longitudinal structure and function data in glaucoma with over
25,000 patients followed over time. If successful, this proposal will lead to a...

## Key facts

- **NIH application ID:** 10225458
- **Project number:** 5R21EY031898-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Felipe Medeiros
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $195,212
- **Award type:** 5
- **Project period:** 2020-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10225458

## Citation

> US National Institutes of Health, RePORTER application 10225458, Objective Quantification of Neural Damage for Screening, Diagnosis and Monitoring of Glaucoma with Fundus Photographs (5R21EY031898-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10225458. Licensed CC0.

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