$100,000, in partnership with Orangetheory Fitness Canada, awarded to Dr. Yana Yunusova at the Sunnybrook Research Institute, Toronto.
At present, no one test or procedure can diagnose ALS. Doctors focus on ruling out other diseases that share some similar initial symptoms. The complexity of the disease and the need for additional diagnostic tests means that for some people, there is a significant delay in making a definitive diagnosis.
Thirty per cent of people with ALS have bulbar-onset ALS. They experience voice and speech changes at disease onset due to a loss of motor neuron function in the corticobulbar area, the area of the brain that controls the muscles of the face, head and neck. Almost all other people living with ALS will have voice and speech difficulties at later stages of the disease.
For this project, Dr. Yana Yunusova will collaborate with Dr. Agessandro Abrahao and Dr. Lorne Zinman at Sunnybrook Health Sciences Centre in Toronto, Dr. Babak Taati at the UHN-Toronto Rehabilitation Institute, and Dr. Sanjay Kalra at the University of Alberta.
Dr. Yunusova and colleagues will use machine learning to train an artificial intelligence (AI) model on voice recordings from people with ALS. They will use voice recordings from people with primary lateral sclerosis, which affects mostly upper motor neurons, people with Kennedy’s disease, which involves only lower motor neurons, and people with ALS affecting both the upper and lower motor neurons. The goal is to create a tool that can separately identify the subtle acoustic features of upper and lower motor neuron disease.
The researchers hope that the new tool will assist existing methods in providing a faster and more accurate way to diagnose people with ALS. It may also help to determine the onset of bulbar symptoms in people with limb-onset disease. Further, it may provide a fast way to identify people who are carriers of specific gene mutations, allowing them to access clinical trials or proven therapies more quickly in the future.