Cristian R. Munteanu, Carlos Fernandez-Lozano, Virginia Mato Abad et al. Expert Systems with Applications, Vol. 42, No. 15-16. (September 2015), pp. 6205-6214, doi:10.1016/j.eswa.2015.03.011. First application of 1H-MRS data and machine-learning to the classification of AD. Classification of individuals affected by different stages of dementia. With two spectroscopic voxel volumes in left hippocampus we achieved a 0.866 AUROC. Classification results are in agreement with previous studies using MRI data. Composition of white and grey matter and cerebrospinal fluid is essential in 1H-MRS. Several magnetic resonance techniques have been proposed as non-invasive imaging biomarkers for the evaluation of disease progression and early diagnosis of Alzheimer’s Disease (AD). This work is the first application of the Proton Magnetic Resonance Spectroscopy 1H-MRS data and machine-learning techniques to the classification of AD. A gender-matched cohort of 260 subjects aged between 57 and 99 years from the Alzheimer’s Disease Research Unit, of the Fundación CIEN-Fundación Reina Sofía has been used. A single-layer perceptron was found for AD prediction with only two spectroscopic voxel volumes (Tvol and CSFvol) in the left hippocampus, with an AUROC value of 0.866 (with TPR 0.812 and FPR 0.204) in a filter feature selection approach. These results suggest that knowing the composition of white and grey matter and cerebrospinal fluid of the spectroscopic voxel is essential in a 1H-MRS study to improve the accuracy of the quantifications and classifications, particularly in those studies involving elder patients and neurodegenerative diseases.
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