[C Light Technologies] Retinal Image-Based Eye Tracker for Neurological Diagnoses

AI-powered eye tracking technology with unprecedented speed and accuracy

Team: Andy Zhang (BIOE), Arjun Bidesi (IEOR), Chiaohan Chuang (BIOE), Younes Sherkat (BIOE)
Advisors: Christy Sheehy (C. Light Technologies), Zach Helft (C. Light Technologies), and Austin Roorda (UC Optometry)

The burden of multiple sclerosis (MS) costs the US healthcare system upwards of $28B annually. Delayed MS diagnoses and unpredictable disease progression create poor patient outcomes and inefficiencies across the healthcare system. There is a lack of objective, cost-effective, and non-invasive tools capable of monitoring disease progression and tracking treatment efficacy. Our industry partner, C. Light, is building an instrument that extracts eye motion over 120x more accurately than current devices on the market and uses disease prediction algorithms to efficiently, objectively, and non-invasively aid in the prognosis and monitoring of MS. C. Light will provide value throughout the care chain of MS: better outcomes and peace-of-mind for patients, treatment feedback and revenue for physicians, reduced overall healthcare costs for payers, and fast, objective outcomes measurements for therapeutic developers

Problem and Motivation

$28B annual cost on US healthcare system from multiple sclerosis (MS)
Poor patient outcomes from unpredictable progression and poor treatment efficacy tracking
Subjective + expensive + invasive tools

Solutions and Outcomes

Tracking Scanning Laser Ophthalmoscope (TSLO):
Retinal motion recording + disease prediction
120x more accurate than current eye-trackers
Fast-acting, 10 second recording
Objective + cost-effective + non-invasive

Capstone Goal

Design transition from research grade to product grade device for pre-commercialization and 510(k) application through various sub-projects:

Gauge Repeatability & Reproducibility
Purpose: Ensure precision of device between trials, operators, and locations
Approach: Setup R&R tests with TSLO to determine areas of variance with MATLAB in the device ability to read and recreate signals from simulated eye motions
Human Factors and Usability
Purpose: Design digital user interface for ease of clinical use and understanding
Approach: Conduct user interviews with neurologists and technicians at UCSF and design interactive software on Sketch with FDA guidance and EN software standards
Disease Prediction
Purpose: Develop capability to predict the likelihood of MS in patients
Approach: Build machine learning algorithm to identify biomarkers using retina motion video data and model training with Python
Risk Analysis and Mitigation Strategy
Purpose: Ensure safety of device and interactions with users and environment
Approach: Brainstorm risks and develop mitigation plans with ISO 14971 guidance and risk management template