# Fully-Automated Lesion Characterization in Ultrawide-Field Retinal Images

> **NIH NIH R44** · EYENUK, INC. · 2021 · $747,733

## Abstract

Abstract
 In this grant application we propose to develop, EyeReadUWF, a fully automated tool for
lesion characterization in ultra-widefield scanning laser ophthalmoscopy (UWF SLO) images. In
recent times non mydriatic UWF SLO imaging has been shown to be a promising alternative to
conventional digital color fundus imaging for grading of diabetic eye diseases, with advantages
including 130°-200° field-of-view showing more than 80% of the retina in a single image, no
need for multiple fields, multiple flashes, or refocusing between field acquisitions, ability to
penetrate media opacities like cataract, and lower rate of ungradable images. UWF SLO images
are particularly suitable for detecting predominantly peripheral lesions (PPLs), which have been
associated with higher risk of diabetic retinopathy (DR) progression. Accurate quantification of
presence and extent of PPLs can only be done by a robust automated tool that is specifically
designed for the pseudo-colored images of UWF SLO modality.
EyeReadUWF will automatically characterize lesions in pseudo colored UWF images while
handling possible artifacts from eyelashes/eyelids and determine the lesion predominance in
peripheral and central regions of UWF image. The ability to accurately quantify the presence
and extent of predominantly peripheral lesions in UWF SLO images can enable clinicians to
develop a more precise DR scoring scheme. This would help identify patients with higher risk of
DR progression and onset of PDR, have a positive impact on diabetic patient management, and
aid drug discovery research.

## Key facts

- **NIH application ID:** 10247802
- **Project number:** 5R44EY028081-03
- **Recipient organization:** EYENUK, INC.
- **Principal Investigator:** Sandeep Bhat
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $747,733
- **Award type:** 5
- **Project period:** 2018-06-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10247802

## Citation

> US National Institutes of Health, RePORTER application 10247802, Fully-Automated Lesion Characterization in Ultrawide-Field Retinal Images (5R44EY028081-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10247802. Licensed CC0.

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