This proposed project is motivated by the fact that comprehensive, broad-scale screening of diabetics for retinopathy is economically prohibitive without the introduction of computer-assisted diagnosis of retinal images. Over 20 million people in the U.S. have diabetes and it is estimated that less than half of those individuals are screened periodically for diabetic retinopathy. 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 human """"""""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. Numerous investigators have developed specific algorithms, each detecting only one specific type of lesion, such as dark lesions, white lesions, or vessel characteristics. These algorithms have been tested using a single modality (pixel format, SLO, standard funduscope, color, red free, etc.) and each new camera can require significant re-tuning of the algorithms. The goal of this project is to demonstrate and then validate an entirely new approach for computer-assisted grading of retinal images. The significance of this proposed research is two-fold. First, by increasing the productivity of reading centers, a much larger population of at-risk diabetics will have access to this service, leading to improved quality of life through early detection and treatment. Second, by providing a validated, robust computer-assisted grading system, all existing reading centers would benefit economically through the added efficiency of our system. Our system does not replace current human readers;it simply allows an increase of throughput of cases by factors of five or more without sacrificing sensitivity and specificity.
Today, about 10 million diabetics do not receive annual eye examinations. Without these examinations early detection of vision threatening retinopathy is not possible. The resulting early loss of vision for many of these diabetics is the leading cause of blindness in the western world. There is an insufficient number of healthcare specialists to perform eye examinations for this population. Without computer-based screening, a broad-scale screening of the population will not be possible.
| 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; 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 |
| 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; 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 |
