Literature DB >> 24323617

Magnetic resonance support vector machine discriminates between Parkinson disease and progressive supranuclear palsy.

Andrea Cherubini1, Maurizio Morelli, Rita Nisticó, Maria Salsone, Gennarina Arabia, Roberta Vasta, Antonio Augimeri, Maria Eugenia Caligiuri, Aldo Quattrone.   

Abstract

BACKGROUND: The aim of the current study was to distinguish patients with Parkinson disease (PD) from those with progressive supranuclear palsy (PSP) at the individual level using pattern recognition of magnetic resonance imaging data.
METHODS: We combined diffusion tensor imaging and voxel-based morphometry in a support vector machine algorithm to evaluate 21 patients with PSP and 57 patients with PD.
RESULTS: The automated algorithm correctly distinguished patients who had PD from those who had PSP with 100% accuracy. This accuracy value was obtained when white matter atrophy was considered. Diffusion parameters combined with gray matter atrophy exhibited 90% sensitivity and 96% specificity.
CONCLUSIONS: Our findings demonstrate that automated pattern recognition can help distinguish patients with PSP from those with PD on an individual basis.
© 2013 Movement Disorder Society.

Entities:  

Keywords:  computer-aided diagnosis; diffusion tensor imaging; progressive supranuclear palsy; support vector machines

Mesh:

Year:  2013        PMID: 24323617     DOI: 10.1002/mds.25737

Source DB:  PubMed          Journal:  Mov Disord        ISSN: 0885-3185            Impact factor:   10.338


  18 in total

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2.  Improved Automatic Morphology-Based Classification of Parkinson's Disease and Progressive Supranuclear Palsy.

Authors:  Aron S Talai; Zahinoor Ismail; Jan Sedlacik; Kai Boelmans; Nils D Forkert
Journal:  Clin Neuroradiol       Date:  2018-09-14       Impact factor: 3.649

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Authors:  Edoardo G Spinelli; Maria Luisa Mandelli; Zachary A Miller; Miguel A Santos-Santos; Stephen M Wilson; Federica Agosta; Lea T Grinberg; Eric J Huang; John Q Trojanowski; Marita Meyer; Maya L Henry; Giancarlo Comi; Gil Rabinovici; Howard J Rosen; Massimo Filippi; Bruce L Miller; William W Seeley; Maria Luisa Gorno-Tempini
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5.  Differentiating Patients with Parkinson's Disease from Normal Controls Using Gray Matter in the Cerebellum.

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Authors:  Carol P Weingarten; Mark H Sundman; Patrick Hickey; Nan-kuei Chen
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7.  Prediction of mild parkinsonism revealed by neural oscillatory changes and machine learning.

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Review 8.  Radiological biomarkers for diagnosis in PSP: Where are we and where do we need to be?

Authors:  Jennifer L Whitwell; Günter U Höglinger; Angelo Antonini; Yvette Bordelon; Adam L Boxer; Carlo Colosimo; Thilo van Eimeren; Lawrence I Golbe; Jan Kassubek; Carolin Kurz; Irene Litvan; Alexander Pantelyat; Gil Rabinovici; Gesine Respondek; Axel Rominger; James B Rowe; Maria Stamelou; Keith A Josephs
Journal:  Mov Disord       Date:  2017-05-13       Impact factor: 10.338

9.  Recent imaging advances in neurology.

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10.  Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results.

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Journal:  Behav Neurol       Date:  2015-11-18       Impact factor: 3.342

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