Abstract #2840

Automatic classification of early-onset neurodegenerative dementia patients using artificial neural networks

Camilla Cividini¹, Federica Agosta¹, Silvia Basaia¹, Luca Wagner¹, Maura Cosseddu², Elisa Canu¹, Stefano Gazzina², Giuseppe Magnani³, Elka Stefanova⁴, Vladimir S. Kostic⁴, Roberto Gasparotti⁵, Alessandro Padovani², Barbara Borroni², and Massimo Filippi^1,3

¹Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy, ²Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy, ³Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy, ⁴Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Yugoslavia, ⁵Neuroradiology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy

We built and validated a convolutional neural network (CNN) to predict the individual diagnosis of early-onset Alzheimer’s disease (EOAD) and behavioral variant of frontotemporal dementia (bvFTD) based on a single T1-weighted image. The analysis showed that CNN procedure was able to discriminate EOAD from healthy controls with an accuracy of 83% (sensitivity=85% and specificity=80%). CNNs differentiated bvFTD patients from controls with an accuracy of 73% (sensitivity=63% and specificity=83%). CNNs provide a powerful tool for the automatic classification of early-onset neurodegenerative dementia and perform well without any prior feature engineering and regardless the variability of imaging protocols and scanners.

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