I-Corps: Machine Learning-Based Diagnosis of Retinal Images with the Aim of Vision Loss Prevention

Texas A&M Engineering Experiment Station, College Station TX

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is to end preventable blindness by increasing access to early diagnostic testing. Utilizing a novel imaging technology and a proven machine learning algorithm, the device allows for point of care diagnosis in a primary care setting at less than half the current cost. By empowering frontline physicians to provide vision-saving eye exams without the need of an eye care specialist, the proposed technology may fundamentally change the current retinal exam landscape, resulting in more efficient and accessible diagnostic testing. This I-Corps Project will yield a portable Artificial Intelligence (AI)-based retinal imaging system consisting of two major components: (1) a hand held ophthalmic device to capture images of the patient's retina, and (2) a machine learning algorithm to classify images of the retina. The hardware solution provides high-resolution images of the retina. The infrared lighting is invisible to the human eye and eliminates the need for pupil dilation as is widely used in the state-of-art fundus imaging currently conducted by medical personnel. The current trained convolutional neural network yields an accuracy of 97% which is on par with the diagnostic accuracy of trained ophthalmologists. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.