Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI.
OBJECTIVE
We present and evaluate a new automated method based on support vector machine (SVM) classification of whole-brain anatomical magnetic resonance imaging to discriminate between patients with Alzheimer's disease (AD) and elderly control subjects.


METHODS
We studied 16 patients with AD [mean age +/- standard deviation (SD) = 74.1 +/- 5.2 years, mini-mental score examination (MMSE) = 23.1 +/- 2.9] and 22 elderly controls (72.3 +/- 5.0 years, MMSE = 28.5 +/- 1.3). Three-dimensional T1-weighted MR images of each subject were automatically parcellated into regions of interest (ROIs). Based upon the characteristics of gray matter extracted from each ROI, we used an SVM algorithm to classify the subjects and statistical procedures based on bootstrap resampling to ensure the robustness of the results.


RESULTS
We obtained 94.5% mean correct classification for AD and control subjects (mean specificity, 96.6%; mean sensitivity, 91.5%).


CONCLUSIONS
Our method has the potential in distinguishing patients with AD from elderly controls and therefore may help in the early diagnosis of AD.
UMR-S 678, Inserm, Paris, France.
Benoît
B
Magnin
Lilia
L
Mesrob
Serge
S
Kinkingnéhun
Mélanie
M
Pélégrini-Issac
Olivier
O
Colliot
Marie
M
Sarazin
Bruno
B
Dubois
Stéphane
S
Lehéricy
Habib
H
Benali
D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology*; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity