Using Machine Learning Towards Decision Support for Refractory Epilepsy Cases

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Between 0.5% to 1.0% of people in North America suffer from epilepsy, and around 30% of patients are drug-resistant. Some drug-resistant patients are candidates for surgery and up to 60% to 70% of patients who undergo surgery become seizure-free. Finding a magnetic resonance imaging (MRI) abnormality on pre-operative imaging increases the chance of surgical success. However, up to 30% to 40% of pre-operative MRIs have no clear lesion in people with drug-resistant epilepsy, and only up to 40% to 50% of non-lesional MRI cases become seizure-free after surgery. The focus of this work was to design decision support tools to help clinicians evaluate patients for surgery. As the first step, we investigated the possibility of segregating MRIs with abnormality from MRIs without any abnormality using Deep Learning models. Such models would help clinicians when they examine MRIs to find an abnormality. Considering the value of predicting surgery results, in our next step, we explored the possibility of predicting the outcome of surgery using MRI and Deep Learning. Our results indicate that both lesional and non-lesional MRIs of patients with epilepsy contain signals that Deep Learning models can harness to predict the operative success., Finally, we explored the possibility of finding an abnormality in MRIs that were reported by radiologists as non-lesional by using Deep Learning.
Epilepsy, Deep Learning, Machine Learning, MRI
Farhoudi, B. (2023). Using machine learning towards decision support for refractory epilepsy cases (Unpublished doctoral thesis). University of Calgary, Calgary, AB.