MRI classification of progressive supranuclear palsy, Parkinson disease and controls using deep learning and machine learning algorithms for the identification of regions and tracts of interest as potential biomarkers
The primary goal of the present study was to compare multiple ML-methods and a DL approach to identify the least necessary set of brain structures to classify PSP vs. controls and PSP vs. PD by ranking them in a hierarchical order of importance. That way, this study demonstrated the potential of AI approaches to MRI as possible diagnostic and scientific tools to differentiate variants of neurodegenerative Parkinsonism.
Authors
- Georg Grön (ORCID: https://orcid.org/0000-0002-0282-3575)
- Lavinia A. Bârlescu
- Günter U. Höglinger (ORCID: https://orcid.org/0000-0001-7587-6187)
- Jan Kassubek (ORCID: https://orcid.org/0000-0002-7106-9270)
- Hans‐Peter Müller (ORCID: https://orcid.org/0000-0001-5706-9381)
- Heiko Volkmann
Institutions
- Universität Ulm (DE)
- Munich Cluster for Systems Neurology (DE)
- Technische Hochschule Ulm (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2024-12-10
- DOI
- https://doi.org/10.1016/j.compbiomed.2024.109518
- Citations
- 16
- Primary Topic
- Parkinson's Disease Mechanisms and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 5.91
Funders
- Deutsches Zentrum für Neurodegenerative Erkrankungen