In this STTR project, we present EyeMark, a set of tools for automated computation of biomarkers for diabetic retinopathy using retinal image photographs. Specifically, we will develop tools for computation of microaneurysm (MA) appearance and disappearance rates (jointly known as turnover rates) for use as a biomarker in monitoring progression of diabetic retinopathy (DR). The availability of a reliable image-based biomarker will have high positive influence on various aspects of DR care, including screening, monitoring progression, drug discovery and clinical research. There is ample published evidence that MA turnover rates are a good predictor of likelihood of progression to more severe retinopathy, establishing MA turnover as an excellent biomarker for diabetic retinopathy. Measuring this quantity involves two steps: careful alignment of current and baseline images, and marking of individual MAs. This process is very time consuming and prone to error, if done by entirely by human graders. The primary goal of this project is to overcome the above limitations by automating both the steps involved in MA turnover measurement: accurate image registration, and MA detection. We will develop end-too-end desktop software for automated computation of MA turnover and also provide intuitive visualization tools for clinicians to more effectively monitor diabetic retinopathy progression.

Public Health Relevance

The proposed tool will greatly enhance the clinical care available to diabetic retinopathy patients by providing an automated tool for computation of a biomarker in a non-invasive manner. This will enable identification of patients who are more likely to progress to severe retinopathy, thus helping prevent vision loss in such patients by timely intervention. Early identification is especially important in face of long backlog of diabetic patients waiting for an eye examination, and the fact that 90% of vision loss can be saved by early identification. The availability of an effective biomarker will also positively influence the drug discovery process by facilitating early and reliable determination of biological efficacy of potential new therapies.

Agency
National Institute of Health (NIH)
Institute
National Center for Advancing Translational Sciences (NCATS)
Type
Small Business Technology Transfer (STTR) Grants - Phase I (R41)
Project #
1R41TR000377-01
Application #
8252674
Study Section
Special Emphasis Panel (ZRG1-ETTN-E (12))
Program Officer
Boller, Francois
Project Start
2012-09-01
Project End
2013-11-30
Budget Start
2012-09-01
Budget End
2013-11-30
Support Year
1
Fiscal Year
2012
Total Cost
$260,857
Indirect Cost
Name
Eyenuk, LLC
Department
Type
DUNS #
832930569
City
Woodland Hills
State
CA
Country
United States
Zip Code
91367