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This work validates the generalizability of MRI-based classification ofAlzheimer's disease (AD) patients and controls (CN) to an external data set andto the task of prediction of conversion to AD in individuals with mildcognitive impairment (MCI). We used a conventional support vector machine (SVM)and a deep convolutional neural network (CNN) approach based on structural MRIscans that underwent either minimal pre-processing or more extensivepre-processing into modulated gray matter (GM) maps. Classifiers were optimizedand evaluated using cross-validation in the ADNI (334 AD, 520 CN). Trainedclassifiers were subsequently applied to predict conversion to AD in ADNI MCIpatients (231 converters, 628 non-converters) and in the independent Health-RIParelsnoer data set. From this multi-center study representing a tertiarymemory clinic population, we included 199 AD patients, 139 participants withsubjective cognitive decline, 48 MCI patients converting to dementia, and 91MCI patients who did not convert to dementia. AD-CN classification based onmodulated GM maps resulted in a similar AUC for SVM (0.940) and CNN (0.933).Application to conversion prediction in MCI yielded significantly higherperformance for SVM (0.756) than for CNN (0.742). In external validation,performance was slightly decreased. For AD-CN, it again gave similar AUCs forSVM (0.896) and CNN (0.876). For prediction in MCI, performances decreased forboth SVM (0.665) and CNN (0.702). Both with SVM and CNN, classification basedon modulated GM maps significantly outperformed classification based onminimally processed images. Deep and conventional classifiers performed equallywell for AD classification and their performance decreased only slightly whenapplied to the external cohort. We expect that this work on external validationcontributes towards translation of machine learning to clinical practice.

Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease