DeepMRS: An End-to-End Deep Neural Network for Dementia Disease Detection using MRS Data
Résumé
Alzheimer’s disease (AD) is the most common form of dementia. Neuroimaging data is an integral part of the clinical assessment providing a way for clinicians to detect brain abnormalities for AD diagnosis. Anatomical MRI has been widely used to assess structural brain atrophy for AD detection and prediction. In addition to structural changes, metabolic changes in some brain regions such as the PCC could be a good bio-marker for an early AD detection. Recently, proton Magnetic Resonance Spectroscopy (1H-MRS) have been proved to be effective to reveal a wealth of brain metabolic information. In this paper, we propose an end-to-end deep leaning Networks for AD and Normal Control (NC) subjects classification using 1H-MRS raw data from thePosterior Cingulate Cortex (PCC) area. This work is the first application of 1H-MRS data with deep-learning technique to the AD detection. Data of 135 subjects, collected in the CHU of Poitiers, are used to learn the DeepMRS network. our classification of patients with AD versus NC subjects achieves an AUC of 94,74% demonstrating a promising dementia detection performance.