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=0.92). 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.
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 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.
|Bhaskaranand, Malavika; Ramachandra, Chaithanya; Bhat, Sandeep et al. (2016) Automated Diabetic Retinopathy Screening and Monitoring Using Retinal Fundus Image Analysis. J Diabetes Sci Technol 10:254-61|