# Discovery and Validation of AMD Biomarkers for Progression Using Deep Learning

> **NIH NIH R21** · DOHENY EYE INSTITUTE · 2020 · $196,250

## Abstract

Project Abstract
Age-related macular degeneration (AMD) is the leading cause of blindness among elderly individuals.
Currently there are no proven effective therapies for treatment of advanced non-neovascular AMD, termed
geographic atrophy (GA). Earlier intervention may be preferable, but this would require identification of those
individuals with the highest risk for progression to atrophy. Over the last two decades, various studies
including ours have identified several optical coherence tomography (OCT)-based factors that appear to
associate with a higher risk for AMD progression.
The central hypothesis of this proposal is that a deep learning - artificial intelligence (AI) construct can
objectively and automatically learn and quantify the most important risk factors, yielding a better prediction of
AMD progression risk than current subjectively specified features. In this proposal, we will first develop an AI-
based system to automatically identify the “subjectively-specified” high risk factors based on individual spectral
domain (SD) OCT 2D scans, and to automatically segment GA (the end-stage outcome variable of AMD) in
OCT 2D en face maps. Subsequently, as a proof-of-concept study of our hypothesis, we will apply an AI-based
“reverse learning” approach to objectively learn and identify AMD high risk factors in longitudinal OCT data.
To achieve these objectives, we will pursue the following specific aims:
Aim 1: Develop and validate an AI approach to classify individual OCT 2D scans as containing or not
containing the pre-specified risk factor(s). In our previous work, we manually identified the presence or
absence of the pre-specific high-risk factors and assigned to a risk score based on the entire OCT volume. Such
approach was time consuming and not precise. In this proposal, an AI algorithm will be applied to detect the
high risk factors from individual OCT scans. Hence, the precision of the scoring system can be greatly
enhanced with high computational complexity.
Aim 2: Develop and validate an AI approach to segment GA lesions from 2D OCT en face maps. For the OCT
volumes having atrophy, we will perform the GA segmentation from the choroidal hypertransmission-resulted
en face map using the multi-scale CNNs.
Aim 3: Develop and validate an AI “reverse learning” approach to objectively identify the high risk factors using
longitudinal OCT data. The “reverse learning” will be based on the multiple CNNs, followed by de-
convolutional networks to identify the high risk factors objectively. Our previous scoring system will possibly
be refined and optimized by the potential inclusion (or substitution) of novel risk factors derived from our
objective AI approaches.
The work in this proposal will be performed retrospectively in SD-OCT images from the image data pool that
the multi-PI Sadda has aggregated over years as the director of the Doheny image reading center.

## Key facts

- **NIH application ID:** 9986814
- **Project number:** 5R21EY030619-02
- **Recipient organization:** DOHENY EYE INSTITUTE
- **Principal Investigator:** Zhihong HU
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $196,250
- **Award type:** 5
- **Project period:** 2019-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9986814

## Citation

> US National Institutes of Health, RePORTER application 9986814, Discovery and Validation of AMD Biomarkers for Progression Using Deep Learning (5R21EY030619-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9986814. Licensed CC0.

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