(18)F-flutemetamol is a positron emission tomography (PET) tracer for in vivo 
amyloid imaging. The ability to classify amyloid scans in a binary manner as 
'normal' versus 'Alzheimer-like', is of high clinical relevance. We evaluated 
whether a supervised machine learning technique, support vector machines (SVM), 
can replicate the assignments made by visual readers blind to the clinical 
diagnosis, which image components have highest diagnostic value according to SVM 
and how (18)F-flutemetamol-based classification using SVM relates to structural 
MRI-based classification using SVM within the same subjects. By means of SVM 
with a linear kernel, we analyzed (18)F-flutemetamol scans and volumetric MRI 
scans from 72 cases from the (18)F-flutemetamol phase 2 study (27 clinically 
probable Alzheimer's disease (AD), 20 amnestic mild cognitive impairment (MCI), 
25 controls). In a leave-one-out approach, we trained the (18)F-flutemetamol 
based classifier by means of the visual reads and tested whether the classifier 
was able to reproduce the assignment based on visual reads and which voxels had 
the highest feature weights. The (18)F-flutemetamol based classifier was able to 
replicate the assignments obtained by visual reads with 100% accuracy. The 
voxels with highest feature weights were in the striatum, precuneus, cingulate 
and middle frontal gyrus. Second, to determine concordance between the gray 
matter volume- and the (18)F-flutemetamol-based classification, we trained the 
classifier with the clinical diagnosis as gold standard. Overall sensitivity of 
the (18)F-flutemetamol- and the gray matter volume-based classifiers were 
identical (85.2%), albeit with discordant classification in three cases. 
Specificity of the (18)F-flutemetamol based classifier was 92% compared to 68% 
for MRI. In the MCI group, the (18)F-flutemetamol based classifier distinguished 
more reliably between converters and non-converters than the gray matter-based 
classifier. The visual read-based binary classification of (18)F-flutemetamol 
scans can be replicated using SVM. In this sample the specificity of 
(18)F-flutemetamol based SVM for distinguishing AD from controls is higher than 
that of gray matter volume-based SVM.