Abstract
Volumetric measurements from magnetic resonance imaging (MRI) scans can be used to predict the future conversion to Alzheimer’s disease (AD) for patients with mild cognitive impairment (MCI). Previous studies achieved good classification results using the volumes of a single as well as multiple scans per subject. The purpose of this study is to evaluate, if and how volumetric features of a baseline (BL) and a follow-up (FU) MRI scan can be combined to improve classification accuracy. For this reason, random forest (RF) models were trained on different volumetric feature sets of 513 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 22 subjects from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) database. The results show that models, which use combinations of both acquisition times yield better accuracies in comparison to the models solely based on FU or BL data. Furthermore, a clear pattern of which combination of representations performs best could not be found. The best model achieves a test classification accuracy of 75.49% (specificity: 80.52%, sensitivity: 60%). Models trained with cognitive test results and MRI data outperform models which use only MRI data. The observed results could not be reproduced on the AIBL dataset.
Louise Bloch
Data Science
My research interests include interpretable machine learning, mutlimodal deep learning, and medical image processing.