Hippocampal shape analysis of Alzheimer disease based on machine learning methods.
OBJECTIVE
Alzheimer disease (AD) is a neurodegenerative disease characterized by progressive dementia. The hippocampus is particularly vulnerable to damage at the very earliest stages of AD. This article seeks to evaluate critical AD-associated regional changes in the hippocampus using machine learning methods.


METHODS
High-resolution MR images were acquired from 19 patients with AD and 20 age- and sex-matched healthy control subjects. Regional changes of bilateral hippocampi were characterized using computational anatomic mapping methods. A feature selection method for support vector machine and leave-1-out cross-validation was introduced to determine regional shape differences that minimized the error rate in the datasets.


RESULTS
Patients with AD showed significant deformations in the CA1 region of bilateral hippocampi, as well as the subiculum of the left hippocampus. There were also some changes in the CA2-4 subregions of the left hippocampus among patients with AD. Moreover, the left hippocampal surface showed greater variations than the right compared with those in healthy control subjects. The accuracies of leave-1-out cross-validation and 3-fold cross-validation experiments for assessing the reliability of these subregions were more than 80% in bilateral hippocampi.


CONCLUSIONS
Subtle and spatially complex deformation patterns of hippocampus between patients with AD and healthy control subjects can be detected by machine learning methods.
Li
S
S
Department of Bioengineering, Beijing University of Aeronautics and Astronautics, and Department of Radiology, Peking University First Hospital, Beijing, People's Republic of China.
Shi
F
F
Pu
F
F
Li
X
X
Jiang
T
T
Xie
S
S
Wang
Y
Y
D000368:Aged; D000544:Alzheimer Disease / Q000473:pathology*; D001185:Artificial Intelligence; D005260:Female; D006624:Hippocampus / Q000473:pathology*; 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
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