TY  - JOUR
AU  - Dyrba, Martin
AU  - Hanzig, Moritz
AU  - Altenstein, Slawek
AU  - Bader, Sebastian
AU  - Ballarini, Tommaso
AU  - Brosseron, Frederic
AU  - Bürger, Katharina
AU  - Cantré, Daniel
AU  - Dechent, Peter
AU  - Dobisch, Laura
AU  - Düzel, Emrah
AU  - Ewers, Michael
AU  - Fliessbach, Klaus
AU  - Glanz, Wenzel
AU  - Haynes, John-Dylan
AU  - Heneka, Michael T
AU  - Janowitz, Daniel
AU  - Keles, Deniz B
AU  - Kilimann, Ingo
AU  - Laske, Christoph
AU  - Maier, Franziska
AU  - Metzger, Coraline D
AU  - Munk, Matthias
AU  - Perneczky, Robert
AU  - Peters, Oliver
AU  - Preis, Lukas
AU  - Priller, Josef
AU  - Rauchmann, Boris
AU  - Roy, Nina
AU  - Scheffler, Klaus
AU  - Schneider, Anja
AU  - Schott, Björn H
AU  - Spottke, Annika
AU  - Spruth, Eike J
AU  - Weber, Marc-André
AU  - Ertl-Wagner, Birgit
AU  - Wagner, Michael
AU  - Wiltfang, Jens
AU  - Jessen, Frank
AU  - Teipel, Stefan J
TI  - Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer's disease.
JO  - Alzheimer's research & therapy
VL  - 13
IS  - 1
SN  - 1758-9193
CY  - London
PB  - BioMed Central
M1  - DZNE-2022-00280
SP  - 191
PY  - 2021
AB  - Although convolutional neural networks (CNNs) 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 as they allow the visualization of key input image features that drive the decision of the model. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge.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 in total 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, thereby allowing intuitive model inspection.Across the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC ≥ 0.91) and moderate accuracy for amnestic MCI versus controls (AUC ≈ 0.74). Relevance maps indicated that hippocampal atrophy was considered 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 ≈ -0.86, p < 0.001).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. The high hippocampus relevance scores as well as the high performance achieved in independent samples support the validity of the CNN models in the detection of AD-related MRI abnormalities. The presented data-driven and hypothesis-free CNN modeling approach might provide a useful tool to automatically derive discriminative features for complex diagnostic tasks where clear clinical criteria are still missing, for instance for the differential diagnosis between various types of dementia.
KW  - Alzheimer Disease: diagnostic imaging
KW  - Cognitive Dysfunction: diagnostic imaging
KW  - Humans
KW  - Magnetic Resonance Imaging: methods
KW  - Neural Networks, Computer
KW  - Neuroimaging: methods
KW  - Alzheimer’s disease (Other)
KW  - Convolutional neural network (Other)
KW  - Deep learning (Other)
KW  - Layer-wise relevance propagation (Other)
KW  - MRI (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:34814936
C2  - pmc:PMC8611898
DO  - DOI:10.1186/s13195-021-00924-2
UR  - https://pub.dzne.de/record/163520
ER  -