Expert-free eye alignment and machine learning for predictive health

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.

Department dc:contributor.department

Program in Media Arts and Sciences (Massachusetts Institute of Technology)

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Swedish, Tristan Breaden

Advisor dc:contributor.advisor

• Ramesh Raskar.

• Program in Media Arts and Sciences ()

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/112543

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/112543