# Deep learning to quantify glaucomatous damage on fundus photographs for teleophthalmology

> **NIH NIH K23** · WAKE FOREST UNIVERSITY HEALTH SCIENCES · 2022 · $194,790

## Abstract

PROJECT SUMMARY/ABSTRACT
Candidate: Atalie Carina Thompson, MD, MPH is a current glaucoma fellow and Heed fellow with a long-term
career goal of becoming an independent clinician-scientist and leader in the field of glaucoma and public health.
She has a long-standing interest in addressing healthcare disparities in medicine, and in improving the diagnosis
of glaucoma and other ophthalmic diseases through imaging technology. While obtaining a medical degree at
Stanford, she received a fellowship to complete a master’s degree in public health with additional higher-level
coursework in biostatistics and epidemiology. Her immediate goal in this proposal is to refine and validate a deep
learning (DL) algorithm capable of quantifying neuroretinal damage on optic disc photographs and then to apply
it in a pilot teleophthalmology program. With a K23 Mentored Patient-Oriented Research Career Development
Award, she will acquire additional didactic training and mentored research experience in glaucoma imaging,
machine learning, biostatistics, clinical research, and the responsible conduct of research. Environment: The
mentorship and expertise of the advisory committee, the extensive resources at the Duke Eye Center and
Departments of Biostatistics and Biomedical Engineering, and the significant institutional commitment will
provide her with the support needed to transition successfully into an independent clinician-scientist. Research:
This proposal will test the hypothesis that a DL algorithm trained with SDOCT detects glaucoma on optic disc
photographs with greater accuracy than human graders. In Specific Aim 1, a DL algorithm that quantifies
neuroretinal damage on optic disc photographs will be refined. The main hypothesis is that the quantitative output
provided by the DL algorithm will allow accurate discrimination of eyes at different stages of the disease
according to standard automated perimetry, and will generate cut-offs suitable for use in a screening setting. In
Specific Aim 2, the short-term repeatability and reproducibility of the DL algorithm in optic disc photographs
acquired over a time period of several weeks will be determined. The hypothesis is that the test-retest variability
of the predictions from the DL algorithm will be similar to the original measurements acquired by SDOCT. In
Specific Aim 3, the DL algorithm will be applied to optic disc photographs obtained during a pilot screening
teleophthalmology program in primary care clinics and assisted living facilities. The hypothesis is that the DL
algorithm will be more accurate than human graders when a full ophthalmic examination is used as the gold
standard. This work will constitute the basis of an R01 grant and will advance our understanding of the application
of deep learning algorithms in glaucoma and teleophthalmology.

## Key facts

- **NIH application ID:** 10348705
- **Project number:** 5K23EY030897-04
- **Recipient organization:** WAKE FOREST UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Atalie C Thompson
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $194,790
- **Award type:** 5
- **Project period:** 2021-06-02 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10348705

## Citation

> US National Institutes of Health, RePORTER application 10348705, Deep learning to quantify glaucomatous damage on fundus photographs for teleophthalmology (5K23EY030897-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10348705. Licensed CC0.

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