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Lead investigator

Collaborators & Co-Investigators

• Michael Brudno, PhD (Co-PI)
• Hanns Lochmüller, MD, PhD
• Issa Alawneh, MD
• Jordi Diaz Manera, MD, PhD

Research Sites & Affiliations

• The Hospital for Sick Children, Toronto, Ontario
• Children’s Hospital of Eastern Ontario (CHEO) Research Institute/University of Ottawa, Ottawa, Ontario
• Newcastle University (UK), ,
• University of Toronto/University Health Network, Toronto, Ontario

Budget: $100,000

Disorders: All Neuromuscular Disorders (cross-cutting)

Research Areas: Understand Diagnosis and Disorder Progression

Abstract: 

Neuromuscular disorders (NMD) can be difficult diagnoses for the most skilled clinician. Multiple approaches may be needed to achieve a final accurate diagnosis. Muscle MRI is a diagnostic tool used more often. However, it requires a high degree of expertise, which limits its use only to highly specialized centers. We propose to use artificial intelligence (AI) and machine learning (ML) to create a user-friendly mobile application. It will be able to do image segmentation, scoring, and pattern recognition. It will help to understand differences between probable diagnoses and insight of genetic results. We will work with MRI databases and key AI and ML experts from the University of Toronto. Our earlier work established the use of AI using manual scoring. It allowed the diagnosis of 10 disorders using MRI. We aim to expand our current database to 7 more childhood NMDs. If successful, we will simplify the use of muscle MRI as a diagnostic tool.

Impact: