Using Machine Learning Towards Decision Support for Refractory Epilepsy Cases

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dc.contributor.advisorMaurer, Frank
dc.contributor.advisorWiebe, Samuel
dc.contributor.authorFarhoudi, Bijan
dc.contributor.committeememberFederico, Paolo
dc.contributor.committeememberJosephson, Colin Bruce
dc.date2023-06
dc.date.accessioned2023-02-01T21:31:48Z
dc.date.available2023-02-01T21:31:48Z
dc.date.issued2023-01-25
dc.description.abstractBetween 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.en_US
dc.identifier.citationFarhoudi, B. (2023). Using machine learning towards decision support for refractory epilepsy cases (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.urihttp://hdl.handle.net/1880/115796
dc.identifier.urihttps://dx.doi.org/10.11575/PRISM/40701
dc.language.isoengen_US
dc.publisher.facultyScienceen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subjectEpilepsyen_US
dc.subjectDeep Learningen_US
dc.subjectMachine Learningen_US
dc.subjectMRIen_US
dc.subject.classificationMedicine and Surgeryen_US
dc.subject.classificationArtificial Intelligenceen_US
dc.subject.classificationComputer Scienceen_US
dc.titleUsing Machine Learning Towards Decision Support for Refractory Epilepsy Casesen_US
dc.typedoctoral thesisen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameDoctor of Philosophy (PhD)en_US
ucalgary.item.requestcopytrueen_US
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