Machine learning for very early Alzheimer´s Disease diagnosis; a 18F-FDG and PiB PET comparison

This paper shows a machine learning approach based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) to compare the diagnostic accuracy on very early Alzheimer´s Disease (AD) patients with ¹⁸F FDG and Pittsburg Compound B (PiB) PET imaging. The Alzheimer´s Disease Neuroimaging Initiative (ADNI) dataset is used for testing, making use of the longitudinal character. Mild Cognitive Impairment (MCI) individuals that after a two years follow up converted into possible AD where used as very early AD patients. While ¹⁸F FDG and PiB have similar diagnostic accuracy in AD, PiB is shown to have higher discriminative power in very early AD with respect to FDG.