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Abstract #3643

Deep Learning on Anatomical Brain MRI to Classify Motor Dysfunction in Parkinson's Disease

Yu-Hsueh Wu1, Yasi Jiang2, Yu-Chun Lo3, Yumei Yue2, Ting Shen2,4, Fu-Shan Jaw1, You-Yin Chen*5, Baorong Zhang*4, and Hsin-Yi Lai*2

1Institute of Biomedical Engineering, National Taiwan University, Taipei City, Taiwan, 2Interdisciplinary Institute of Neuroscience and Technology, Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou City, China, 3The PhD Program for Neural Regenerative Medicine, Taipei Medical University, Taipei City, Taiwan, 4Department of neurology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou City, China, 5Department of Biomedical Engineering, National Yang Ming University, Taipei City, Taiwan

We introduced an innovative two-staged deep artificial neural network (DNN) model focusing on diagnostic prediction of Parkinson’s disease (PD) using T1-weighted images, given a training set consisting of cortical thickness, surface area, grey matter volume and corresponding clinical scales, our proposed model was trained to classify the PD with different motor symptoms and performed the diagnostic prediction on basis of generated clinical scales. Results showed our DNN classifier and generator reached the averaged accuracy of 100% and 97.9%, respectively. To our knowledge, our technique was the first to tackle the classification of motor dysfunction in PD from anatomical brain MRI.

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