0000000000345224

AUTHOR

Lydia Yahia-cherif

showing 1 related works from this author

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…

0301 basic medicinemedicine.medical_specialtyParkinson's diseaseParkinson's diseasemultiple system atrophyProgressive supranuclear palsyDiagnosis Differential03 medical and health sciences0302 clinical medicinePhysical medicine and rehabilitationParkinsonian DisordersmedicineHumansmultimodal magnetic resonance imagingReceiver operating characteristicmedicine.diagnostic_testbusiness.industryParkinsonismMagnetic resonance imagingprogressive supranuclear palsymedicine.diseaseMagnetic Resonance Imaging3. Good healthnervous system diseasesmachine learning algorithm030104 developmental biologyDiffusion Tensor ImagingNeurologyCategorizationnervous systemCohort[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]Neurology (clinical)Supranuclear Palsy Progressivebusiness030217 neurology & neurosurgeryDiffusion MRI
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