Machine Learning in SaRS-COV-2 and COVID Medical Imaging – Forum, November 18, 2022

1024 389 MaLMIC - Machine Learning in Medical Imaging Consortium

Join the Machine Learning in Medical Imaging Consortium (MaLMIC) for an opportunity to network

Machine Learning in SaRS-COV-2 and COVID Medical Imaging

Friday, November 18, 2022
3:00 to 4:30 p.m. ET

Douglas Lee, a cardiologist and clinician scientist, will present his work on machine learning in COVID-related risk analysis using large-scale population data. Simon Graham, a senior scientist, will present his recent work on COVID-related medical imaging. The talks will be followed by a discussion focused on lessons learned, opportunities to collaborate, and sharing of resources.

Interested in joining? Please contact us.

Machine Learning in SaRS-COV-2 and COVID Medical Imaging

Friday, November 18, 2022
3:00 to 4:30 p.m. ET

Douglas Lee, a cardiologist and clinician scientist, will present his work on machine learning in COVID-related risk analysis using large-scale population data. Simon Graham, a senior scientist, will present his recent work on COVID-related medical imaging. The talks will be followed by a discussion focused on lessons learned, opportunities to collaborate, and sharing of resources.

Interested in joining? Please contact us.

Using Machine Learning to Gain Insights on SARS CoV-2 in Long-term Care

Douglas Lee, MD, PhD
Senior Scientist, ICES and Toronto General Hospital Research Institute

Long-haul COVID19, the NeuroCOVID19 Project and Opportunities for Machine-Learning

Simon Graham, PhD
Senior Scientist, Sunnybrook Research Institute

Talk summary: Since the beginning of the pandemic, Dr. Simon Graham has led a team of investigators at Sunnybrook Health Sciences Centre, Baycrest Centre and more recently St. Michael’s Hospital in Toronto to conduct NeuroCOVID19: a detailed neuroimaging study of long-haul COVID19 effects on brain and behaviour. Simon will explain the NeuroCOVID19 project rationale, experimental protocol, preliminary findings and discuss the machine learning opportunities that are available with this dataset, towards improved treatment and management of long-haul symptoms.