# Differential artery-vein analysis in OCT angiography for objective classification of diabetic retinopathy

> **NIH NIH R01** · UNIVERSITY OF ILLINOIS AT CHICAGO · 2022 · $250,000

## Abstract

Summary: This is an administrative supplement application for the R01 project, entitled ‘Differential artery-vein
analysis in OCT angiography for objective classification of diabetic retinopathy’ (R01EY030842). The purpose of
this supplement application is to add artificial intelligence (AI) topic research for enhanced retinal image
construction and automated artery-vein analysis. Potential impact of the proposed AI topic research is
twofold: 1) to advance AI technology in eye health; 2) to training graduate students interested in AI technology.
One underrepresented American-born-Vietnamese student and two African/black students from underdeveloped
countries will be involved in this project. It is known that diabetic retinopathy (DR) can target retinal arteries and
veins differently. Therefore, differential artery-vein analysis can provide better performance of DR detection and
classification. With this active R01 support, we have developed algorithms to achieve differential artery-vein
analysis in OCTA for better clinical management of DR. For this supplement application, we propose to expand
deep learning (DL) based AI approaches to foster clinical deployments of differential artery-vein analysis. The
proposed AI research topics will naturally provide a useful platform to foster the education training of
underrepresented students in biomedical engineering and AI ophthalmology. The first AI topic is transfer
learning OCTA construction and DL artery-vein analysis. In traditional OCTA machine, multiple OCT image
acquisitions are required, and subsequent correlation algorithms are employed. However, due to the requirement
of multiple image acquisitions, there is a tradeoff between imaging speed and resolution/field-of-view in OCTA.
We have recently demonstrated the feasibility of transfer learning OCTA construction from a single-volumetric-
scan animal (mouse) OCT. We propose to validate DL based OCTA construction and artery-vein analysis using
clinical OCT of human retina. The second AI topic is to validate a portable eye imager for high-fidelity artery-
vein imaging and AI ophthalmology. Using clinical OCTA, we have demonstrated differential artery-vein analysis
for improved DR detection. However, clinical OCTA machine is typically bulky and expensive, limiting their
application for telemedicine in rural and underserved areas. Moreover, currently available clinical OCTA has a
field of view (FOV) typically within 10-20o, corresponding to 3-6 mm retinal region. We propose to validate a
portable eye imager with AI transfer learning for high-fidelity artery-vein analysis at capillary level. This portable,
low-cost eye imager is based on our recently demonstrated high dynamic range (HDR) fundus camera and the
AI transfer learning construction in AI topic 1 study. We anticipate that this portable eye imager will provide
clinical OCTA level resolution for differential artery-vein analysis with a snapshot FOV up to 67o, corresponding
to 20 mm retinal reg...

## Key facts

- **NIH application ID:** 10680158
- **Project number:** 3R01EY030842-03S1
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT CHICAGO
- **Principal Investigator:** Jennifer Irene Lim
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $250,000
- **Award type:** 3
- **Project period:** 2020-02-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10680158

## Citation

> US National Institutes of Health, RePORTER application 10680158, Differential artery-vein analysis in OCT angiography for objective classification of diabetic retinopathy (3R01EY030842-03S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10680158. Licensed CC0.

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