AIMC Topic: Supranuclear Palsy, Progressive

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Machine learning-based decision tree classifier for the diagnosis of progressive supranuclear palsy and corticobasal degeneration.

Neuropathology and applied neurobiology Apr 7, 2021
AIMS: This study aimed to clarify the different topographical distribution of tau pathology between progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD) and establish a machine learning-based decision tree classifier.
Aged Aged, 80 and over Male Middle Aged Decision Trees Humans Machine Learning Brain Female Nerve Degeneration Inclusion Bodies Corticobasal Degeneration Diagnosis, Differential tau Proteins Neurofibrillary Tangles Supranuclear Palsy, Progressive
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Discriminating progressive supranuclear palsy from Parkinson's disease using wearable technology and machine learning.

Gait & posture Feb 10, 2020
BACKGROUND: Progressive supranuclear palsy (PSP), a neurodegenerative conditions may be difficult to discriminate clinically from idiopathic Parkinson's disease (PD). It is critical that we are able to do this accurately and as early as possible in o...
Wearable Electronic Devices Logistic Models Parkinson Disease Diagnosis, Differential Gait Analysis Aged, 80 and over Sensitivity and Specificity Male Middle Aged Supranuclear Palsy, Progressive Humans Machine Learning Algorithms Female Aged
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Development and validation of the automated imaging differentiation in parkinsonism (AID-P): a multicentre machine learning study.

The Lancet. Digital health Aug 27, 2019
BACKGROUND: Development of valid, non-invasive biomarkers for parkinsonian syndromes is crucially needed. We aimed to assess whether non-invasive diffusion-weighted MRI can distinguish between parkinsonian syndromes using an automated imaging approac...
Anisotropy Diffusion Magnetic Resonance Imaging Multiple System Atrophy Supranuclear Palsy, Progressive United States Parkinson Disease Austria Germany Humans Parkinsonian Disorders Machine Learning Aged Biomarkers Male Brain Middle Aged Female Cohort Studies Reproducibility of Results
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Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging: a proof of concept study.

European radiology Jul 1, 2019
OBJECTIVES: To evaluate the diagnostic performance of deep learning with the convolutional neural networks (CNN) to distinguish each representative parkinsonian disorder using MRI.
Humans Neural Networks, Computer Retrospective Studies Female Aged Parkinsonian Disorders Proof of Concept Study Multiple System Atrophy Supranuclear Palsy, Progressive Deep Learning ROC Curve Magnetic Resonance Imaging Parkinson Disease Diagnosis, Differential Aged, 80 and over Adult Male Middle Aged
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Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI.

NeuroImage. Clinical Mar 6, 2019
Neuromelanin sensitive magnetic resonance imaging (NMS-MRI) has been crucial in identifying abnormalities in the substantia nigra pars compacta (SNc) in Parkinson's disease (PD) as PD is characterized by loss of dopaminergic neurons in the SNc. Curre...
Diagnosis, Differential Substantia Nigra Neuroimaging Melanins Multiple System Atrophy Deep Learning Supranuclear Palsy, Progressive Biomarkers Image Processing, Computer-Assisted Humans Female Magnetic Resonance Imaging Sensitivity and Specificity Parkinson Disease Male Middle Aged
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Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines.

Parkinsonism & related disorders Nov 28, 2017
BACKGROUND AND PURPOSE: In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classi...
Parkinsonian Disorders Proton Magnetic Resonance Spectroscopy Supranuclear Palsy, Progressive Male Humans Middle Aged Brain Image Processing, Computer-Assisted Female Support Vector Machine Aged Magnetic Resonance Imaging
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Automated Imaging Differentiation for Parkinsonism.

JAMA neurology May 1, 2025
IMPORTANCE: Magnetic resonance imaging (MRI) paired with appropriate disease-specific machine learning holds promise for the clinical differentiation of Parkinson disease (PD), multiple system atrophy (MSA) parkinsonian variant, and progressive supra...
Humans Prospective Studies Female Aged Male Diagnosis, Differential Parkinsonian Disorders Diffusion Magnetic Resonance Imaging Multiple System Atrophy Middle Aged Supranuclear Palsy, Progressive Cohort Studies Support Vector Machine Magnetic Resonance Imaging Parkinson Disease
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Faster, More Practical, but Still Accurate: Deep Learning for Diagnosis of Progressive Supranuclear Palsy.

Radiology. Artificial intelligence May 1, 2024
Supranuclear Palsy, Progressive Humans Deep Learning
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Deep Learning-based Approach for Brainstem and Ventricular MR Planimetry: Application in Patients with Progressive Supranuclear Palsy.

Radiology. Artificial intelligence May 1, 2024
Purpose To develop a fast and fully automated deep learning (DL)-based method for the MRI planimetric segmentation and measurement of the brainstem and ventricular structures most affected in patients with progressive supranuclear palsy (PSP). Materi...
Image Interpretation, Computer-Assisted Cerebral Ventricles Brain Stem Supranuclear Palsy, Progressive Humans Aged Retrospective Studies Male Female Middle Aged Reproducibility of Results Deep Learning Magnetic Resonance Imaging
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Outgroup Machine Learning Approach Identifies Single Nucleotide Variants in Noncoding DNA Associated with Autism Spectrum Disorder.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Autism spectrum disorder (ASD) is a heritable neurodevelopmental disorder affecting 1 in 59 children. While noncoding genetic variation has been shown to play a major role in many complex disorders, the contribution of these regions to ASD susceptibi...
Genetic Variation CpG Islands RNA, Untranslated Microsatellite Repeats Supranuclear Palsy, Progressive Female Male Case-Control Studies Cohort Studies Computational Biology Child Gene Regulatory Networks Logistic Models Genetic Predisposition to Disease MicroRNAs Humans Polymorphism, Single Nucleotide DNA Machine Learning Genetic Association Studies Phenotype Whole Genome Sequencing Autism Spectrum Disorder
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