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Figure 1
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Team 04

Team Members

Faculty Advisor

Cooper Ladd
Shreedula Balakrishnan

Guoan Zheng


UConn School of Engineering

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Device and mobile app development for retinal image diagnosis

Retinal imaging is a key tool in disease diagnosis however it is expensive and inconvenient to do. Retinal exams are financially inaccessible to the poor as many do not have eye insurance and these retinal devices are expensive, ranging in thousands of dollars. It is an inconvenient and time-consuming process for the patient to schedule and travel to an eyecare center, receive an exam and wait for a diagnosis by an ophthalmologist. Hence, our goal is to create a handheld device for retinal imaging that can work to diagnose a disease using deep learning entirely from home. The handheld imaging device will consist of a camera, lens, light source and a beam splitter to capture the retinal image and will pair with an app on the cell phone to diagnose for Diabetic Retinopathy. By making our device and companion app easy to use, diagnosis for Diabetic Retinopathy can be self-administered and does not need the resources/guidance of an optometrist.