New Methods for Youth Concussion Diagnosis and Assessment
Creating the world’s largest imaging database of childhood concussion to identify imaging biomarkers that can be used in machine learning algorithms to predict fast recovery or prolonged symptoms
Magnetic resonance (MR) image of the brain – part of the largest imaging database on childhood concussion in the world
Concussion is a very common injury in children and youth. Currently, the diagnosis of concussion is based largely on a person’s symptoms because we lack any objective biomarker. While most children recover well from a concussion, many have ongoing problems. However, we are unable to predict who will recover and who will not.
BME researchers are studying 1,000 children--700 with concussion and 300 with minor orthopedic injuries—who have been recruited in the emergency department at five pediatric emergency departments across Canada. The study includes comprehensive neuroimaging with MRI and will represent the largest imaging database on childhood concussion in the world. UCalgary researchers are identifying imaging biomarkers that can be used in machine learning algorithms to differentiate concussions from other injuries and predict who will recover quickly versus those who will have prolonged symptoms.
Dr. Keith Yeates (left), Dr. Catherine Lebel (middle) and Dr. Ashley Harris (right)
Using diffusion imaging, white matter pathways can be virtually reconstructed within the brain to measure their connectivity. Here, the cingulum (orange), fornix (blue) and association tracts (pink/purple), major white matter tracts within the brain,
Partners
University of Alberta/Stollery Children’s Hospital
University of Ottawa/Children’s Hospital of Eastern Ontario
University of British Columbia/BC Children’s Hospital
University of Montreal/Ste Justine Hospital (all members of Pediatric Emergency Research Canada network)