In this small business innovations research (SBIR) project, we present aiArt: Advanced Image Analysis Tools for Diabetic Retinopathy Telemedicine applications. aiArt (pronounced eye-art), with its automated image analysis tools and user-friendly telemedicine web-interface, will enable exponential expansion of diabetic retinopathy screenings, thus fulfilling a significant health need as the number of people with diabetes climbs over the years. Latino population is genetically more prone to diabetes. Factors such as lack of awareness, lack of insurance coverage, and lack of access to expert clinicians greatly increase this disparity population's vulnerability to blindness due to DR. The situation is particularly grim in Los Angeles County, where there is a backlog of several thousand patients waiting to see an ophthalmologist, causing very long appointment wait times (often over six months). To help reduce risk of vision loss in this population, we propose to use advanced image analysis algorithms in conjunction with existing telemedicine initiatives to enable faster screening, allow reprioritization of ophthalmologist appointments, and to provide patient education tools. Our automated image analysis algorithms represent cutting-edge of research in image processing, computer vision, and machine learning. The analysis engine will be closely integrated with simple, easy-to-use web-based telemedicine infrastructure provided by an existing, popular, telemedicine initiative, EyePACS.
The proposed image analysis tools will greatly reduce the cost of diabetic retinopathy screening, and with its web and mobile phone accessible interface will drive an expansion of diabetic retinopathy screening, making it accessible to disparity populations (such as Latinos) which are not currently being screened due to socio-economic factors. The proposed tools will also enable quick turnaround time for screening, thus further helping prevent blindness due to diabetes complications.