  Background: Although convolutional neural networks (CNN) achieve high
diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on
magnetic resonance imaging (MRI) scans, they are not yet applied in clinical
routine. One important reason for this is a lack of model comprehensibility.
Recently developed visualization methods for deriving CNN relevance maps may
help to fill this gap. We investigated whether models with higher accuracy also
rely more on discriminative brain regions predefined by prior knowledge.
  Methods: We trained a CNN for the detection of AD in N=663 T1-weighted MRI
scans of patients with dementia and amnestic mild cognitive impairment (MCI)
and verified the accuracy of the models via cross-validation and in three
independent samples including N=1655 cases. We evaluated the association of
relevance scores and hippocampus volume to validate the clinical utility of
this approach. To improve model comprehensibility, we implemented an
interactive visualization of 3D CNN relevance maps.
  Results: Across three independent datasets, group separation showed high
accuracy for AD dementia vs. controls (AUC$\geq$0.92) and moderate accuracy for
MCI vs. controls (AUC$\approx$0.75). Relevance maps indicated that hippocampal
atrophy was considered as the most informative factor for AD detection, with
additional contributions from atrophy in other cortical and subcortical
regions. Relevance scores within the hippocampus were highly correlated with
hippocampal volumes (Pearson's r$\approx$-0.86, p<0.001).
  Conclusion: The relevance maps highlighted atrophy in regions that we had
hypothesized a priori. This strengthens the comprehensibility of the CNN
models, which were trained in a purely data-driven manner based on the scans
and diagnosis labels.
