Expert-free eye alignment and machine learning for predictive health

Abstract

This thesis documents the development of an "expert-free" device in order to realize a system for scalable screening of the eye fundus. The goal of this work is to demonstrate enabling technologies that remove dependence on expert operators and explore the usefulness of this approach in the context of scalable health screening. I will present a system that includes a novel method for eye self-alignment and automatic image analysis and evaluate its effectiveness when applied to a case study of a diabetic retinopathy screening program. This work is inspired by advances in machine learning that makes accessible interactions previously confined to specialized environments and trained users. I will also suggest some new directions for future work based on this expert-free paradigm.