Explainable prediction of Parkinson’s disease in a large multimodal database

Camacho Camacho, Milton Ivan

Explainable prediction of Parkinson’s disease in a large multimodal database

dc.contributor.advisor	Forkert, Nils Daniel
dc.contributor.advisor	Monchi, Oury
dc.contributor.author	Camacho Camacho, Milton Ivan
dc.contributor.committeemember	Ismail, Zahinoor
dc.contributor.committeemember	LeVan, Pierre
dc.date	2024-05
dc.date.accessioned	2023-09-22T14:45:24Z
dc.date.available	2023-09-22T14:45:24Z
dc.date.issued	2023-09-08
dc.description.abstract	Parkinson’s disease (PD) is the second most common neurodegenerative disease affecting millions of people all over the world. Accurate diagnosis is important to enable prompt interventions that can improve disease prognosis. However, the heterogeneity of PD renders an accurate diagnosis challenging, especially early in the disease phase when symptoms are known to be subtle. Thus, it is imperative to obtain reliable, non-invasive, in-vivo biomarkers for PD diagnosis. Within this context, the overarching aim of this work is to develop accurate explainable deep learning models trained from a large multimodal magnetic resonance imaging (MRI) database to classify PD and healthy controls and capable of identifying structural changes associated with PD. Therefore, the objectives of this thesis are: (1) to investigate the use of T1-weighted brain MRI as a biomarker of macro-structural changes associated with PD; (2) to investigate a combination of T1-weighted and diffusion tensor MRI as a fusion of micro- and macro-structural brain morphology and its relationship with PD. To achieve these objectives, one of the largest multi-center imaging databases of over 2,000 PD and control subjects was pooled and preprocessed as the first step. Second, an explainable deep learning model was developed to accurately classify PD patients while revealing important brain regions. Third, a multimodal explainable deep learning model was trained enabling a more in-depth understanding of the interplay between the micro- and macro-structural properties of specific brain regions and the disease. The results of this work offer an important insight into structural brain changes as non-invasive, in-vivo biomarkers of PD through an in-depth analysis of associated brain regions using large multicenter data. Deep learning models are proposed to provide generalizable and robust PD classification resulting in a 0.87 and 0.89 area under the receiver operating characteristic curve for the best unimodal and multimodal approaches, respectively. Lastly, explainability methods identified brain regions in line with current knowledge of the disease providing further evidence for the clinical utility of the developed methods. This work presents relevant findings and novel methodologies that could aid improving PD diagnosis and the acceptance of computer-aided diagnosis systems targeting PD.
dc.identifier.citation	Camacho Camacho, M. I. (2023). Explainable prediction of Parkinson’s disease in a large multimodal database (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/117011
dc.identifier.uri	https://doi.org/10.11575/PRISM/41854
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
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	Parkinson's disease
dc.subject	Magnetic resonance imaging
dc.subject	Artificial intelligence
dc.subject	Machine learning
dc.subject	Deep learning
dc.subject	Computer aided diagnosis
dc.subject	Multimodal imaging
dc.subject.classification	Education--Health
dc.subject.classification	Bioinformatics
dc.subject.classification	Radiology
dc.subject.classification	Artificial Intelligence
dc.subject.classification	Engineering--Biomedical
dc.subject.classification	Psychology--Clinical
dc.subject.classification	Computer Science
dc.title	Explainable prediction of Parkinson’s disease in a large multimodal database
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 require a thesis withhold – I need to delay the release of my thesis due to a patent application, and other reasons outlined in the link above. I have/will need to submit a thesis withhold application.