Research Project
8782362
DESCRIPTION (provided by applicant): In this SBIR project, we present EyeApp, a smartphone-based end-to-end point-of- care diabetic retinopathy diagnostic device comprising of a smartphone-attachable retinal imaging camera and validated software application enabling fully-automated screening of diabetic retinopathy (DR). DR is the leading cause of new-onset blindness in working-age adults in the industrialized world today. Studies show that in 90% of the cases vision loss can be prevented if DR is diagnosed at an early stage. Recommended annual screening is expected to significantly improve outcome for diabetic patients, but even in the developed world, majority of the diabetics do not regularly have the annual screening due to several limiting factors. To make things worse, the current cost of screening all diabetics to detect those with early DR is very high because: (a) most patients will have normal exams (thus, causing inefficient use of specialist's time), (b) retinal imaging equipment is very expensive, and (c) trained technicians must operate the imaging equipment. In this project we address these critical limitations by employing cellphone retinal camera attachment, the Ocular CellScope, developed by our collaborators at UCB/UCSF, which simply attaches to an iPhone, doesn't need a trained technician to operate, will obtain wide-field images, and will cost several orders of mag- nitude less than the current tabletop retinal imaging cameras. Diabetic patients can attach the camera to their smartphones and with some assistance (untrained friend, spouse, or nurse) obtain retinal video for each eye using an app residing on the same phone. This app guides the image capture to ensure that best quality frames are obtained, and also ensure all areas of the eye are imaged enabling wide-field imaging. EyeApp's validated algorithms output a near-instant DR screening recommendation score. EyeApp is con- ceptualized as a culmination of over three years of research and development at Eyenuk (on computerized DR screening) and at UCB/UCSF (on smartphone retinal camera) that has already produced functional prototypes of critical technology modules. This phase I project focusses on seamless integration of these proven modules (the camera hardware and the diagnostic software) via several novel ideas.
