# SCH: Multimodal Retina Image Alignment and Applications

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2024 · $273,134

## Abstract

PROJECT SUMMARY (See instructions):
Overview: Progress in the characterization and treatment of retinal disease involves epidemiological and
natural history studies which include genetic and environmental risk factor evaluation as well as clinical
trials. As treatments advance, it is important to be able to scientifically analyze and interpret a large
amount of information that can be procured from different areas and even points on the retina and
evaluate retinal structure and function over time and in response to therapies. Currently there is a
proliferation of technology to provide data and this data comes from many instruments and from different
companies. It is increasingly difficult for any one person or reading center to evaluate this information.
The goal of this proposal is to develop deep-learning based multi modal retinal image processing methods
to help the ophthalmologist to quickly detect and diagnose diseases.
Intellectual Merit: As treatments advance, it is important to be able to scientifically analyze and interpret a
large amount of information that can be procured from different areas and even points on the retina and
evaluate retinal structure and function over time and in response to therapies. Currently there is a
proliferation of imaging technology producing images from many instruments and from different
companies. It is increasingly difficult for any one person or reading center to reliably review the multiple
types of imaging available in a patient with a retinal disease nor overlay these on each other to properly
analyze retinal structure and function and determine correlations and predictive value of these tests to
clinical outcomes and covariates like sex, age, race and concurrent medications. The ability to co-localize
all of this data and use artificial intelligence (Al) to help with these analytics will advance the field of
understanding and treating retinal disease. The objective of this proposal is to develop deep-learning
based multimodal retinal image registration methods to help the ophthalmologist to quickly detect and
diagnose retinal diseases.

## Key facts

- **NIH application ID:** 10916222
- **Project number:** 5R01EY033847-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Cheolhong An
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $273,134
- **Award type:** 5
- **Project period:** 2021-09-30 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10916222

## Citation

> US National Institutes of Health, RePORTER application 10916222, SCH: Multimodal Retina Image Alignment and Applications (5R01EY033847-04). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10916222. Licensed CC0.

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