This proposed project is motivated by the fact that comprehensive, broad-scale screening for eye diseases such as age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy (DR), including advanced stages, is economically prohibitive without the introduction of computer-assisted diagnosis of retinal images. According to the CDC approximately 80 million people in the U.S. have some form of eye disease, including 20 million diabetics at risk for retinopathy, 60 million at risk for glaucoma, and 13 million diagnosed with AMD. It is estimated that less than half of those individuals with diabetes are screened periodically for DR. Lack of medical coverage and access to healthcare providers imposes major obstacles for nearly 10 million diabetics. Creating an affordable and accessible solution to providing screening services to these diabetics presents a significant challenge to the healthcare community. A comprehensive screening program for U.S. citizens utilizing ophthalmologists, optometrists, or other trained specialists, readers, to grade each case would be prohibitively expensive. The solution is to implement a computer-assisted technology similar to other medical applications, such as mammograms and Pap smears;that would provide comprehensive, periodic screening of our at-risk population. Our business models show a 400% decrease in costs with the integration of automation into today manual screening methods. The study will be made possible through the implementation of the infrastructure for a reading center in South Texas where it is estimated that nearly 200,000 diabetics reside and over 50% do not receive annual examinations. The center will be a demonstration site where the efficacy of the software can be documented for submission to the FDA for pre-market approval (PMA). The goal is to show that a center's through put number of cases screened per time period will increase fivefold through the use of a hybrid (automation-human graders) approach with sensitivity that is comparable or better than solely human-based screening. The significance of this proposed research is two-fold. First, by increasing the productivity of reading centers through automation, a much larger population of at-risk individuals will have access to this service, leading to improved productivity and quality of life through early detection and treatment. Second, by providing to the FDA, a system that is highly effective and sensitive to advanced disease, the issue of safety is essentially eliminated.

Public Health Relevance

Today, the CDC reports that there are about 80 million people in the US who have some form of eye disease. Diabetic retinopathy, age-related macular degeneration, and glaucoma are the leading causes of blindness. There is some form of intervention for each of these eye diseases that can result in the extension of useful vision, if detected early. Our automated eye disease screening system will make examination available to millions of Americans at risk for eye disease available without major impact on our healthcare system. This is possible only through automation as an adjunct to examinations by eye care specialists.

National Institute of Health (NIH)
National Eye Institute (NEI)
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
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Special Emphasis Panel (ZRG1-ETTN-E (12))
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Wujek, Jerome R
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Visionquest Biomedical, LLC
United States
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Agurto, Carla; Yu, Honggang; Murray, Victor et al. (2015) A multiscale decomposition approach to detect abnormal vasculature in the optic disc. Comput Med Imaging Graph 43:137-49
Agurto, Carla; Murray, Victor; Yu, Honggang et al. (2014) A multiscale optimization approach to detect exudates in the macula. IEEE J Biomed Health Inform 18:1328-36
Agurto, Carla; Joshi, Vinayak; Nemeth, Sheila et al. (2014) Detection of hypertensive retinopathy using vessel measurements and textural features. Conf Proc IEEE Eng Med Biol Soc 2014:5406-9
Yu, H; Barriga, E S; Agurto, C et al. (2012) Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets. IEEE Trans Inf Technol Biomed 16:644-57
Agurto, Carla; Yu, Honggang; Murray, Victor et al. (2012) Detection of neovascularization in the optic disc using an AM-FM representation, granulometry, and vessel segmentation. Conf Proc IEEE Eng Med Biol Soc 2012:4946-9
Agurto, Carla; Barriga, E Simon; Murray, Victor et al. (2011) Automatic detection of diabetic retinopathy and age-related macular degeneration in digital fundus images. Invest Ophthalmol Vis Sci 52:5862-71