Deep learning methods for classifying disease subtypes in multiple sclerosis based on clinical imaging and non-imaging data
| dc.contributor.advisor | Zhang, Yunyan | |
| dc.contributor.advisor | Bento, Mariana Pinheiro | |
| dc.contributor.author | Soleymani, Mahshid | |
| dc.contributor.committeemember | Forkert, Nils Daniel | |
| dc.contributor.committeemember | MacDonald, Matthew Ethan | |
| dc.date | 2024-11 | |
| dc.date.accessioned | 2024-09-24T22:17:00Z | |
| dc.date.available | 2024-09-24T22:17:00Z | |
| dc.date.issued | 2024-09-23 | |
| dc.description.abstract | Multiple sclerosis (MS) is a common inflammatory demyelinating and neurodegenerating disease of the central nervous system impacting over 2.8 million people worldwide. Most people start MS with a relapsing-remitting form (RRMS), yet no two persons have the same disease course. Many of them will develop a secondary-progressive course (SPMS) despite treatment, causing dramatic health and socioeconomic consequences. Early accurate measurement of disease activity will permit early effective treatment for improved prognosis. But there is no established method to classify these two subtypes beforehand clinically. By leveraging the power of deep learning such as convolutional neural networks (CNNs), this project aims to optimize personalized disease characterization using standard clinical data especially brain magnetic resonance imaging (MRI). Specifically, based on 140 clinical participants with RRMS or SPMS, the research targets phenotype prediction through a series of development and validation processes. These included data optimization, model development and testing based on both 2D- and 3D- CNN models, and model interpretation using a recognized method called gradient-class activation mapping (Grad- CAM). Results showed that axial images normalized with a Z-score like approach were most feasible. Both the 2D and 3D models achieved >80% accuracy in predicting RRMS and SPMS, where combining both MRI and clinical variables appeared to perform better than either data type alone. The Grad-CAM analysis helped discern critical brain areas related to each MS subtype. These findings underscore the potential of deep learning based completely on clinical care data to detect disease activity, marking early diagnosis and personalized treatment possible. | |
| dc.identifier.citation | Soleymani, M. (2024). Deep learning methods for classifying disease subtypes in multiple sclerosis based on clinical imaging and non-imaging data (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | |
| dc.identifier.uri | https://hdl.handle.net/1880/119889 | |
| dc.language.iso | en | |
| dc.publisher.faculty | Schulich School of Engineering | |
| dc.publisher.institution | University of Calgary | |
| dc.rights | University 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. | |
| dc.subject.classification | Artificial Intelligence | |
| dc.subject.classification | Engineering--Biomedical | |
| dc.title | Deep learning methods for classifying disease subtypes in multiple sclerosis based on clinical imaging and non-imaging data | |
| dc.type | master thesis | |
| thesis.degree.discipline | Engineering – Biomedical | |
| thesis.degree.grantor | University of Calgary | |
| thesis.degree.name | Master of Science (MSc) | |
| ucalgary.thesis.accesssetbystudent | I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible. |