Validation of convolutional neural network relevance maps for revealing patterns of Alzheimer’s disease in MRI scans

Abstract

AbstractBackgroundAlthough machine learning approaches achieve high diagnostic accuracy for detecting Alzheimer’s disease (AD) based on MRI scans, they are not applied in clinical routine due to a lack of suitable methods for model comprehensibility and interpretability. Recently developed visualization methods for convolutional neural networks (CNN) may fill this gap. We trained a CNN model to detect AD based on T1‐weighted MRI and implemented a web application to provide an intuitive visualization of relevance maps. The aim of this study was to evaluate the association of relevance scores and hippocampus volume to validate the clinical utility of this approach.MethodMRI scans for 254 cognitively normal controls (CN), 219 patients with amnestic mild cognitively impairment (MCI), and 189 patients with AD were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). MRI scans were tissue‐segmented and normalized using VBM8. Hippocampal volume was extracted from the gray matter (GM) maps. Subsequently, GM maps and hippocampal volume were corrected for effects of total intracranial volume, age, gender and scanner magnetic field strength using a linear model. Thirty‐two coronal slices covering the hippocampus were selected and the corresponding GM residual maps entered into the CNN. The model was evaluated using ten‐fold cross‐validation. Finally, we derived activation maps using the layer‐wise relevance propagation (LRP) algorithm and calculated the sum of hippocampus relevance scores.ResultWe obtained highly accurate results of an AUC of 93±6% for AD vs. CN. For MCI vs. CN, group separation was lower with an AUC of 74±9%. The relevance maps for four exemplary patients are shown in Fig. 1. Relevance maps confirmed that hippocampal atrophy is the most informative region for AD detection with minor contributions from other cortical and subcortical regions. Relevance scores within the hippocampus were correlated with hippocampal volume with a Pearson correlation of r=‐0.79 (Fig.2).ConclusionOur CNN model yielded high accuracy when detecting AD and a medium level of accuracy for mild cognitive impairment. In general, the relevance maps showed the expected regions. The high association of hippocampus relevance scores and volume indicate a high validity of the CNN model as implemented in our intuitively usable web application.