AIMC Topic: Parkinsonian Disorders
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Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline.
European radiology
Jul 22, 2023
OBJECTIVES: This study aims at a fully automatic pipeline for measuring the magnetic resonance parkinsonism index (MRPI) using deep learning methods.
Deep learning regressor model based on nigrosome MRI in Parkinson syndrome effectively predicts striatal dopamine transporter-SPECT uptake.
Neuroradiology
May 20, 2023
PURPOSE: Nigrosome imaging using susceptibility-weighted imaging (SWI) and dopamine transporter imaging using I-2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane (I-FP-CIT) single-photon emission computerized tomography (SPECT) can eval...
Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning.
European journal of nuclear medicine and molecular imaging
May 19, 2022
PURPOSE: This work attempts to decode the discriminative information in dopamine transporter (DAT) imaging using deep learning for the differential diagnosis of parkinsonism.
Deep Learning-Based Neuromelanin MRI Changes of Isolated REM Sleep Behavior Disorder.
Movement disorders : official journal of the Movement Disorder Society
Feb 1, 2022
BACKGROUND: Isolated REM sleep behavior disorder (iRBD) is considered a prodromal stage of parkinsonism. Neurodegenerative changes in the substantia nigra pars compacta (SNc) in parkinsonism can be detected using neuromelanin-sensitive MRI.
Multi-View Mouse Social Behaviour Recognition With Deep Graphic Model.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Jun 11, 2021
Home-cage social behaviour analysis of mice is an invaluable tool to assess therapeutic efficacy of neurodegenerative diseases. Despite tremendous efforts made within the research community, single-camera video recordings are mainly used for such ana...
Revealing posturographic profile of patients with Parkinsonian syndromes through a novel hypothesis testing framework based on machine learning.
PloS one
Feb 25, 2021
Falling in Parkinsonian syndromes (PS) is associated with postural instability and consists a common cause of disability among PS patients. Current posturographic practices record the body's center-of-pressure displacement (statokinesigram) while the...
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...
Development and Validation of the Automated Imaging Differentiation in Parkinsonism (AID-P): A Multi-Site Machine Learning Study.
The Lancet. Digital health
Aug 27, 2019
BACKGROUND: There is a critical need to develop valid, non-invasive biomarkers for Parkinsonian syndromes. The current 17-site, international study assesses whether non-invasive diffusion MRI (dMRI) can distinguish between Parkinsonian syndromes.
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.
Deep Learning
ROC Curve
Magnetic Resonance Imaging
Humans
Adult
Proof of Concept Study
Neural Networks, Computer
Male
Retrospective Studies
Multiple System Atrophy
Aged
Parkinson Disease
Middle Aged
Female
Aged, 80 and over
Supranuclear Palsy, Progressive
Diagnosis, Differential
Parkinsonian Disorders
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