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 Shen^2,4, 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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