Predicting Disease Deterioration in Multiple Sclerosis
Funding Amount
$306,763
Affiliation(s)
University of Calgary
Geographic Region(s) / Province(s)
Alberta
Researcher(s)
Impact Goal(s)
Advance Treatment and Care
Understand and Halt Disease Progression
Background: A current limitation in multiple sclerosis (MS) care is the ability to accurately predict whether a person’s MS will worsen and when, since the disease trajectory differs for each individual with MS. Being able to better predict a person’s disease trajectory can influence the healthcare provider and patient’s treatment choices and decisions.
Overview: This project aims to establish a new more accurate method of predicting the 5-year risk of disability worsening scores in people with relapsing-remitting MS using a type of artificial intelligence called ‘deep learning.’ Deep learning is a relatively new type of machine learning that has demonstrated success with image data analysis and disease prediction for individuals. In this project, Dr. Yunyan Zhang and team will develop a deep learning approach that combines data from a patient’s brain pathology, clinical information, and brain magnetic resonance (MRI) imaging to predict the risk of disease deterioration. The approach will be built out and evaluated with a clinical cohort of 5,632 MS patients.
Impact: In the longer-term, this work aims to address this critical gap in patient care by identifying those at greatest risk of disease worsening, ultimately leading to more personalized care to prevent and better manage the onset of advanced disease.
Project Status: In Progress