Big Data Approaches AI and Clinical Brain Imaging

Big Data Approaches for Synergy between Artificial Intelligence and Clinical Brain Imaging

Project Team: Sam Doesburg (Biomedical Physiology and Kinesiology, SFU), Urs Ribary (Behavioral and Cognitive Neuroscience Institute, SFU), Vasily Vakorin (Biomedical Physiology and Kinesiology, SFU), George Medvedev (Fraser Health Authority)

Breakthroughs in computational imaging and machine learning can effectively aid in diagnosing patient conditions, match treatment with best outcomes and predict patients at risk for disease or hospital readmission. The amount of data that is collected in the process of a diagnostic workup and treatment of patients is tremendous. But, the organization and evaluation of such amount of data is impossible without collaboration of clinicians and computational data experts.

Strengthening collaborative research with strategic partners in clinical health is key to delivering insights. The Fraser Health Authority services Canada’s fastest growing municipalities while its Royal Columbian Hospital provides complex neurological, neurosurgical and neuro-radiological care to approximately 1.5 million people in the region. Adopting data-driven approaches to brain imaging enables clinicians to better predict and classify neurological states and facilitate better treatment decisions for the patient.

This transformative project enables SFU to pursuing translational research and new collaborations in health care, and will also be highly beneficial for clinical imaging communities with the goal to improve health care in British Columbia.