Concussion are extremely common in deployment and in military and civilian activities (i.e.) sports). Persisting symptoms that make up “post-concussive syndrome” (PCS) including headaches, balance difficulties , depression and anxiety can occur in 10-15% of cases. The diagnosis of concussion and PCS is currently based on a patient’s report of their symptoms and a physical exam. Research, including our own, has explored the value of specific tests including those that use eye movements, neuropsychological tests and MRI. Although useful in the research setting, we do not understand the value of these tests when used together and need to know what aspects of those tests are most valuable in developing future tools that distinguish those who are injured from those who are not. For this research we will utilize a dataset collected over the last four years, that contains MRI, neuropsychological, eye movement and other data from concussed, PCS and non-injured individuals. We will apply machine learning based methods (convolutional and long short term memory neural networks) to analyze the data to define more sensitive and specific tests. These tools may be used in both a military and civilian setting allowing for more personalized treatment and recovery programs thereby lessening the burden of concussion and PCS.

Industry Partner(s):IBM Canada Ltd.

Academic Institution:University of Toronto, Ryerson University

Academic Researcher: Michael Cusimano

Co-PI Name: Alireza Sadeghian

Focus Areas: Health

Platforms: Cloud, GPU