Development of a data processing and machine learning pipeline using non-invasive diffuse reflectance spectroscopy eye imaging for Alzheimer disease screening

Alzheimer’s disease (AD) is a progressive neurodegenerative disease and is currently the most common cause of dementia, affecting approximately 50 million individuals worldwide. RetiSpec has developed a non-invasive, label-free retinal imager that can uniquely detect and quantify AD biomarkers – namely, signatures of A? aggregates. The combination of hyperspectral imaging and machine learning may be a quick, simple, and cost-effective method that can be used to identify AD retinal biomarkers years before the emergence of clinical symptoms. In this project novel tissue spectroscopy methods will be developed to detect the presence of amyloid plaque and soluble oligomers, and to quantify changes in microvasculature. To this end machine learning methods will be applied to data acquired in more than 100 patients to develop predictive models able to automatically screen patients for AD as part of regular eye exams.

Faculty Supervisor:

Frédéric Leblond

Student:

Partner:

Retispec

Discipline:

Engineering

Sector:

Manufacturing

University:

Polytechnique Montréal

Program: