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Abstract #0685

Automated Imaging Classification Based On Volumetric Analysis: Application On Primary Progressive Aphasia

Andreia Vasconcellos Faria1,2, Kyrana Tsapkini3, Jennifer Crinion4, Hangyi Jiang1, Xin Li1, Kenichi Oishi1, Peter van Zijl1, Michael Miller5, Argye Hillis3, Susumu Mori1

1Radiology, Johns Hopkins University, Baltimore, MD, USA; 2Radiology, State University of Campinas, Campinas, SP, Brazil; 3Neurology, Johns Hopkins University, Baltimore, MD, USA; 4Institute of Cognitive Neuroscience, University College London; 5Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

Based on large-deformation diffeomorphic metric mapping and Atlas-based analysis, we developed a method to capture the anatomical features and classifying primary progressive aphasia (PPA) patients. Principal component analysis, multivariate techniques and predictive modeling were applied to a 32 PPA patients and 27 controls. The variables used to create and test the classification model were selected among the volumes of the 211 regions obtained from the automated 3D segmentation. The percentage of correct classification was 83% after two-level cross-validation. The results from this automated quantitative analysis can be user-friendly displayed and this method can be applied to routine clinical practice