Individual detection of patients with Parkinson disease using support vector machine analysis of diffusion tensor imaging data: initial results

Haller, Sven; Badoud, Simon; Nguyen, Duy; Garibotto, Valentina; Lövblad, Karl-Olof; Burkhard, Pierre

doi:10.3174/ajnr.A3126

Scientific article

English

Individual detection of patients with Parkinson disease using support vector machine analysis of diffusion tensor imaging data: initial results

ContributorsHaller, Sven; Badoud, Simon; Nguyen, Duy; Garibotto, Valentina

; Lövblad, Karl-Olof; Burkhard, Pierre

Published inAmerican journal of neuroradiology, vol. 33, no. 11, p. 2123-2128

Publication date2012

Abstract

Brain MR imaging is routinely performed in the work-up of suspected PD, yet its role is essentially limited to the exclusion of other pathologies. We performed a pattern-recognition analysis based on DTI data to detect subjects with PD at the individual level.

Keywords

Aged
Algorithms
Brain/pathology
Diffusion Magnetic Resonance Imaging/methods
Female
Humans
Image Enhancement/methods
Image Interpretation, Computer-Assisted/methods
Male
Parkinson Disease/diagnosis
Pattern Recognition, Automated/methods
Pilot Projects
Reproducibility of Results
Sensitivity and Specificity
Support Vector Machines

Affiliation entities

Faculté de médecine / Section de médecine clinique / Département de radiologie et informatique médicale

Research groups

Citation (ISO format)

HALLER, Sven et al. Individual detection of patients with Parkinson disease using support vector machine analysis of diffusion tensor imaging data: initial results. In: American journal of neuroradiology, 2012, vol. 33, n° 11, p. 2123–2128. doi: 10.3174/ajnr.A3126

Article (Published version)

Identifiers

PID : unige:31974
DOI : 10.3174/ajnr.A3126
PMID : 22653326

Journal ISSN0195-6108

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Individual detection of patients with Parkinson disease using support vector machine analysis of diffusion tensor imaging data: initial results

Brain MR imaging is routinely performed in the work-up of suspected PD, yet its role is essentially limited to the exclusion of other pathologies. We performed a pattern-recognition analysis based on DTI data to detect subjects with PD at the individual level.

Technical informations