0000000000294597
AUTHOR
Jean-christophe Corvol
showing 3 related works from this author
Automated classification of neurodegenerative parkinsonian syndromes using multimodal magnetic resonance imaging in a clinical setting
2020
ABSTRACTBackgroundSeveral studies have shown that machine learning algorithms using MRI data can accurately discriminate parkinsonian syndromes. Validation under clinical conditions is missing.ObjectivesTo evaluate the accuracy for the categorization of parkinsonian syndromes of a machine learning algorithm trained with a research cohort and tested on an independent clinical replication cohort.Methods361 subjects, including 94 healthy controls, 139 patients with PD, 60 with PSP with Richardson’s syndrome, 41 with MSA of the parkinsonian variant (MSA-P) and 27 with MSA of the cerebellar variant (MSA-P), were recruited. They were divided into a training cohort (n=179) scanned in a research en…
Lack of Accredited Clinical Training in Movement Disorders in Europe, Egypt, and Tunisia.
2020
BACKGROUND: Little information is available on the official postgraduate and subspecialty training programs in movement disorders (MD) in Europe and North Africa.OBJECTIVE: To survey the accessible MD clinical training in these regions.METHODS: We designed a survey on clinical training in MD in different medical fields, at postgraduate and specialized levels. We assessed the characteristics of the participants and the facilities for MD care in their respective countries. We examined whether there are structured, or even accredited postgraduate, or subspecialty MD training programs in neurology, neurosurgery, internal medicine, geriatrics, neuroradiology, neuropediatrics, and general practic…
Automated Categorization of Parkinsonian Syndromes Using Magnetic Resonance Imaging in a Clinical Setting
2020
Background Machine learning algorithms using magnetic resonance imaging (MRI) data can accurately discriminate parkinsonian syndromes. Validation in patients recruited in routine clinical practice is missing. Objective The aim of this study was to assess the accuracy of a machine learning algorithm trained on a research cohort and tested on an independent clinical replication cohort for the categorization of parkinsonian syndromes. Methods Three hundred twenty-two subjects, including 94 healthy control subjects, 119 patients with Parkinson's disease (PD), 51 patients with progressive supranuclear palsy (PSP) with Richardson's syndrome, 35 with multiple system atrophy (MSA) of the parkinsoni…