Objective Diagnosis & Quantification of Retinal Disease

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DESCRIPTION (provided by applicant): The current diagnosis and quantification of retinal diseases, such as age-related macular degeneration, relies on the subjective interpretation of color retinal images and fluorescein angiograms by trained experts. The subjectivity of the diagnostic process has complicated the appropriate application of clinical trial results to clinical practice. With rapid advances in the genotyping of retinal disease and the development of therapies which target disease at the molecular level, the need for accurate, reproducible, and precise phenotyping of retinal disease has become critical. The development of high-resolution digital fundus imaging systems, advanced mathematics, and high-speed computing has now made complete quantification of retinal lesions feasible. To solve this important problem, a team of retinal image interpretation experts, mathematicians, computer scientists, and image processing experts has been assembled. The process of retinal image analysis and quantification is divided into three major components: (1) image selection and preparation, (2) image alignment or registration, and (3) image segmentation, identification, and quantification. In the first (R21) phase of this proposal, the aim is to develop a prototype system to answer important feasibility questions related to each of these three components. Accomplishing this aim will require the development of image quality assessment metrics, shade-correction algorithms, global warping functions, and a database of intensity profiles for various retinal structures. In the second (R33) phase of this proposal, the prototype system from the R21 phase will be refined, optimized, and validated against the existing gold standard (expert human interpretation) and other objective and subjective clinical parameters. At the end of this proposed research project, a validated technology will have been developed for the reproducible, accurate, and objective diagnosis, classification, and quantification of retinal disease. This technology will be of great value in future clinical and pre-clinical studies, and in everyday clinical practice.