# Deep Learning Approaches to Detect Glaucoma and Predict Progression from Spectral Domain Optical Coherence Tomography

> **NIH NIH K99** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $117,347

## Abstract

Project Abstract / Summary
Primary open angle glaucoma (POAG) is a leading cause of blindness in the United States and worldwide. It is
estimated that over 2.2 million Americans suffer from POAG and that over 130,000 are legally blind from the
disease. As the population ages, the number of people with POAG in the United States will increase to over 3.3
million in 2020 and worldwide to an estimated 111.8 million by 2040. POAG is a progressive disease associated
with characteristic functional and structural changes that clinicians use to diagnose and monitor the disease.
Over the past several years, spectral domain optical coherent tomography (SDOCT) has become the standard
tool for measuring structure in POAG. This 3D imaging modality provides a wealth of information about retinal
structure and POAG-related retinal layers. This large amount of data is hard for clinicians to interpret and use
effectively to help guide treatment decisions. Instead, summary metrics such as average layer thicknesses are
used to reduce SDOCT images to a handful of values. While these metrics are useful, they can be difficult to
interpret and they throwaway important information regarding voxel intensity and texture, relationships across
retinal layers, and the overall 3D structure of the retina. Relying too heavily on these metrics limits our ability to
gain a deeper understanding structural contributions to POAG, the relationship between structure and visual
function, and how structural (and functional) changes progress in POAG. Recent advances in artificial
intelligence and deep learning, however, offer new data-driven tools and techniques to interpret 3D SDOCT
images and learn from the large SDOCT datasets being collected in clinics around the world. This proposal will
apply state-of-the-art deep learning techniques to 3D SDOCT data in order to (1) develop more accurate
POAG detection tools, (2) reveal structure-function relationships, and (3) predict structural and
functional progression in POAG.
This proposal also details a training plan to help the PI transition from a postdoctoral scholar to an independent
researcher. The mentored phase of this award will be supervised by the primary mentor, Dr. Linda Zangwill, and
a multidisciplinary mentoring team including Dr. Robert Weinreb (Ophthalmology), Dr. David Kriegman
(Computer Science and Engineering), and Dr. Armin Schwartzman (Biostatistics). Performing the proposed
research, formal coursework, and mentored career development will the provide the PI with highly sought-
after skills and experience to help ensure a successful transition into independence.

## Key facts

- **NIH application ID:** 10055661
- **Project number:** 1K99EY030942-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Mark Christopher
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $117,347
- **Award type:** 1
- **Project period:** 2020-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10055661

## Citation

> US National Institutes of Health, RePORTER application 10055661, Deep Learning Approaches to Detect Glaucoma and Predict Progression from Spectral Domain Optical Coherence Tomography (1K99EY030942-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10055661. Licensed CC0.

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