  This work validates the generalizability of MRI-based classification of
Alzheimer's disease (AD) patients and controls (CN) to an external data set and
to the task of prediction of conversion to AD in individuals with mild
cognitive impairment (MCI). We used a conventional support vector machine (SVM)
and a deep convolutional neural network (CNN) approach based on structural MRI
scans that underwent either minimal pre-processing or more extensive
pre-processing into modulated gray matter (GM) maps. Classifiers were optimized
and evaluated using cross-validation in the ADNI (334 AD, 520 CN). Trained
classifiers were subsequently applied to predict conversion to AD in ADNI MCI
patients (231 converters, 628 non-converters) and in the independent Health-RI
Parelsnoer data set. From this multi-center study representing a tertiary
memory clinic population, we included 199 AD patients, 139 participants with
subjective cognitive decline, 48 MCI patients converting to dementia, and 91
MCI patients who did not convert to dementia. AD-CN classification based on
modulated GM maps resulted in a similar AUC for SVM (0.940) and CNN (0.933).
Application to conversion prediction in MCI yielded significantly higher
performance for SVM (0.756) than for CNN (0.742). In external validation,
performance was slightly decreased. For AD-CN, it again gave similar AUCs for
SVM (0.896) and CNN (0.876). For prediction in MCI, performances decreased for
both SVM (0.665) and CNN (0.702). Both with SVM and CNN, classification based
on modulated GM maps significantly outperformed classification based on
minimally processed images. Deep and conventional classifiers performed equally
well for AD classification and their performance decreased only slightly when
applied to the external cohort. We expect that this work on external validation
contributes towards translation of machine learning to clinical practice.
