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 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 triage patients with higher risk of DR progression and onset of PDR, have a positive impact on diabetic patient management, and aid drug discovery research.

Public Health Relevance

The proposed tool, EyeReadUWF, will perform automated lesion characterization in ultra- widefield scanning laser ophthalmoscopy (UWF SLO) images to quantify the presence and extent of predominantly peripheral lesions (PPLs), which have been associated with higher risk of diabetic retinopathy (DR) progression. To the best of our knowledge, no commercial automated analysis tool is currently indicated for UWF SLO images. Once clinically validated, the tool can enable clinicians to triage patients with higher risk of DR progression and onset of PDR, have a positive impact on diabetic patient management, and aid drug discovery research.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
Project #
1R43EY028081-01A1
Application #
9559582
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Wujek, Jerome R
Project Start
2018-06-01
Project End
2019-05-31
Budget Start
2018-06-01
Budget End
2019-05-31
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Eyenuk, Inc.
Department
Type
DUNS #
832930569
City
Woodland Hills
State
CA
Country
United States
Zip Code
91367