Improving the clinical oculomotor assessment for patients with multiple sclerosis

Clinicians’ ability to detect abnormal eye-movements has powerful implications for the diagnosis, monitoring of disease progression and evaluation of therapeutic outcomes in patients with multiple sclerosis. Despite the advantages of the oculomotor assessment, standard clinical practices are limited and do not capture the dynamic aspects of eye-movement. Sophisticated methods have been developed in the laboratory setting for this purpose, but these are often expensive and hard to adapt to the naturalistic clinical setting. Taking advantage of the digital cameras readily available in mobile devices, lnnodem Neurosciences has developed a cost effective and user–friendly solution to quantify eye-movement perturbations. If successful, this would reduce the need for expensive laboratory exams for the diagnosis and monitoring of disease progression in patients with multiple sclerosis. Using modern artificial intelligence tools, we aim to improve the algorithms used for the extraction of eye-movement features. Furthermore, we seek to explore the diverse and heterogeneous symptomatic manifestations of multiple sclerosis from a neuro-ophthalmological point of view.

Faculty Supervisor:

Bratislav Misic

Student:

Partner:

Innodem Neurosciences

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

McGill University

Program: