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.

Journal: Computers in biology and medicine

PMID: 39662313

Abstract

BACKGROUND: Quantitative magnetic resonance imaging (MRI) analysis has shown promise in differentiating neurodegenerative Parkinsonian syndromes and has significantly advanced our understanding of diseases like progressive supranuclear palsy (PSP) in recent years.

Authors

Heiko Volkmann

Department of Neurology, University of Ulm, Ulm, Germany. Electronic address: heiko.volkmann@uni-ulm.de.
Günter U Höglinger

Department of Neurology, University Hospital Gießen and Marburg, Marburg, Germany.
Georg Grön

Section for Neuropsychology and Functional Imaging, Dept. of Psychiatry III, University of Ulm, Germany. Electronic address: georg.groen@uni-ulm.de.
Lavinia A Bârlescu

Department of Neurology, University of Ulm, Ulm, Germany. Electronic address: lavinia.barlescu@uni-ulm.de.
Hans-Peter Müller

Department of Neurology, University of Ulm, Ulm, Germany.
Jan Kassubek

Department of Neurology, University of Ulm, Ulm, Germany. jan.kassubek@uni-ulm.de.

Keywords

Aged Algorithms Biomarkers Brain Deep Learning Diffusion Tensor Imaging Female Humans Image Interpretation, Computer-Assisted Machine Learning Magnetic Resonance Imaging Male Middle Aged Parkinson Disease Supranuclear Palsy, Progressive

External Resources

View on PubMed Access via DOI PubMed (39662313)

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.

Abstract

Authors

Keywords

External Resources

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