The director of the probabilistic vision group and medical imaging lab at McGill University spoke about how these learning methods can be used to predict future lesion activity and disability progression in MS.
“There's a lot of room for machine learning to have similar success within the context of medicine, from diagnosis to prediction and precision medicine and so on. However, a lot of these techniques are not regularly used in the clinic.”
Machine learning and deep learning algorithms have begun to make an impact on the process of collecting patient outcome measurements, and now they have come into the field of multiple sclerosis (MS).
In a presentation at the Americas Committee for Treatment and Research in MS (ACTRIMS) Forum in Dallas, Texas, Tal Arbel, PhD, helped to show how these learning methods can be used to predict future lesion activity and disability progression in MS. Notably, the professor in the Department of Electrical and Computer Engineering at McGill University explained since new T2 and gadolinium-enhancing lesions are indicators of disease activity, predicting their appearance in future images could help predict disease worsening, as well as treatment responders.
To find out more about what work is being done right now by the director of the probabilistic vision group and medical imaging lab and her group, NeurologyLive spoke with her at the ACTRIMS Forum.
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