You are here

Automated Image-based Biomarker Computation Tools for Diabetic Retinopathy

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 2R42TR000377-02
Agency Tracking Number: R42TR000377
Amount: $1,472,859.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: NCATS
Solicitation Number: PA13-235
Timeline
Solicitation Year: 2014
Award Year: 2014
Award Start Date (Proposal Award Date): 2014-09-26
Award End Date (Contract End Date): 2017-06-30
Small Business Information
21860 BURBANK BLVD STE 160
Woodland Hills, CA 91367-7409
United States
DUNS: 832930569
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 KAUSHAL SOLANKI
 (805) 455-9771
 solanki@eyenuk.com
Business Contact
 KAUSHAL SOLANKI
Phone: (805) 455-9771
Email: solanki@eyenuk.com
Research Institution
 LA BIOMED RES INST/ HARBOR UCLA MED CTR
 
1124 WEST CARSON ST
TORRANCE, CA 90502-2006
United States

 Domestic nonprofit research organization
Abstract

DESCRIPTION provided by applicant In this STTR project we present EyeMark a set of advanced image analysis tools for automated computation of biomarkers for diabetic retinopathy DR using retinal fundus images 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 quantifying DR progression risk 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 Measuring MA turnover involves two labor intensive 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 entirely by human graders The primary goal of this project is to overcome these limitations by automating both the steps involved in MA turnover measurement accurate image registration and MA detection In Phase I we have designed and developed a MA turnover computation prototype tool that robustly registers longitudinal images even with multiple lesion changes and effectively detects MAs lesion level AUROC The tool provides graceful degradation to confounding image factors by reporting MA turnover as a range thereby capturing the inherent confidence in MA detection By the end of Phase II we will develop a clinically validated end to end desktop software for robust automated computation of MA turnover biomarker that can work on the cloud to produces results in near constant time for large datasets and also provide intuitive visualization tools for clinicians to more effectively monitor DR progression PUBLIC HEALTH RELEVANCE The proposed tool EyeMark will greatly enhance the clinical care available to diabetic retinopathy DR patients by providing an automated tool for computation of an image based reliable DR biomarker in a non invasive manner This will enable identification of patients who are at higher risk 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 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

* Information listed above is at the time of submission. *

US Flag An Official Website of the United States Government