The objective of this project is to develop the Comprehensive Eye Evaluation Algorithm (CEEvA), a system to achieve blindness prevention in a cost effective way. CEEvA uses fundus images and available personal medical data in the electronic medical record (medical history and laboratory tests) to automatically screen not only for DR, but also for POAG and AMD in populations at higher risks. CEEvA targets screening for three eye diseases in the following populations: 1) DR screening annually for every diabetic, 2) POAG screening for those with diabetes, or with family history of POAG, who are African?American over the age of 40, or who are Hispanic?American aged 65 and older, and 3) AMD screening for Whites over 65 years. The motivation of this project is to increase the efficiency of eye disease screening by detecting the main causes of blindness in the US population. Currently, diabetic retinopathy (DR), age- related macular degeneration (AMD) and primary open?angle glaucoma (POAG) affect 7.7 million, 2.1 million, and 2.7 million people, respectively. Direct medical costs in the United States due to these three eye diseases are $14.5 billion per year. The effectiveness of screening for DR has generated multiple successful screening programs. Although screening for only AMD and POAG has not been demonstrated to be cost effective in the population at large, various studies have demonstrated that by screening the populations at higher risk for these diseases, vision loss is reduced by up to 40%. The objectives of this project will be accomplished through two specific aims.
In Aim 1 we will incorporate medical data to complement and augment the prototype fundus?based algorithms previously developed for DR, AMD, and glaucoma detection.
In Aim 2, our goal is to validate CEEvA software in an accurate and reliable way by using 407 cases of retrospective data. At the end of Phase I we will have a system ready for validation in a Phase II prospective study in preparation for an FDA clinical trial.
In the United States, diabetic retinopathy (DR), age?related macular degeneration (AMD), and primary open?angle glaucoma (POAG) are the main causes of blindness affecting over 12 million people. In this project we will develop a ?Comprehensive Eye Evaluation Algorithm? (CEEvA), a system that uses fundus images, personal information and available laboratory tests to automatically screen for DR, POAG, and AMD at PCP practices. This project addresses the public health need of providing more extensive eye screening to the population at high risk of vision loss, thus potentially preventing blindness.
