Automatic classification of MR scans in Alzheimer's disease
University College London · Mayo Clinic · +2 more institutions
Abstract
To be diagnostically useful, structural MRI must reliably distinguish Alzheimer's disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly…
Citation impact
- FWCI
- 37.00
- Percentile
- 100%
- References
- 65
Authors
10- SKStefan KlöppelCorresponding
University College London, Mayo Clinic, Mayo Clinic in Arizona, Wellcome Centre for Human Neuroimaging
- CMCynthia M. Stonnington
Mayo Clinic, Mayo Clinic in Arizona, Wellcome Centre for Human Neuroimaging, University College London
- CCCarlton Chu
Wellcome Centre for Human Neuroimaging, Mayo Clinic, Mayo Clinic in Arizona, University College London
- BDBogdan Draganski
Mayo Clinic, Mayo Clinic in Arizona, University College London, Wellcome Centre for Human Neuroimaging
- RIRachael I. Scahill
Wellcome Centre for Human Neuroimaging, University College London, Mayo Clinic, Mayo Clinic in Arizona
Topics & keywords
- Support vector machine
- Medical diagnosis
- Frontotemporal lobar degeneration
- Grey matter
- Alzheimer's disease
- Medicine
- Artificial intelligence
- Pattern recognition (psychology)