Parkinson's Disease (PD) is one of the most common types of neurological diseases caused by progressive degeneration of dopamin- ergic neurons in the brain. Even though there is no fixed cure for this neurodegenerative disease, earlier diagnosis followed by earlier treatment can help patients have a better quality of life. Magnetic Resonance Imag- ing (MRI) has been one of the most popular diagnostic tool in recent years because it avoids harmful radiations. In this paper, we investi- gate the plausibility of using MRIs for automatically diagnosing PD. Our proposed method has three main steps : 1) Preprocessing, 2) Fea- ture Extraction, and 3) Classification. The FreeSurfer library is used for the first and the second steps. For classification, three main types of classifiers, including Logistic Regression (LR), Random Forest (RF) and Support Vector Machine (SVM), are applied and their classification abil- ity is compared. The Parkinsons Progression Markers Initiative (PPMI) data set is used to evaluate the proposed method. The proposed system prove to be promising in assisting the diagnosis of PD.
Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease
Weill Cornell Medical College, Cornell University（康奈尔大学威尔康奈尔医学院）；University of Connecticut（康涅狄格大学）；Feinberg School of Medicine, Northwestern University（西北大学范伯格医学院）；Michigan State University（密歇根州立大学）
Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks
University of Waterloo（滑铁卢大学）；Ludwig-Maximilians-Univ. München（慕尼黑大学）；Technical University of Munich（慕尼黑工业大学）；Schön Klinik München Schwabing（慕尼黑施瓦宾 Schön 医院）
Estimation of Orofacial Kinematics in Parkinson's Disease: Comparison of 2D and 3D Markerless Systems for Motion Tracking
KITE — Toronto Rehabilitation Institute — University Health Network（凯特大西洋康复研究所——多伦多康复研究所——大学医疗网络）；University of Toronto（多伦多大学）；Sunnybrook Research Institute（森尼布鲁克研究所）
Parkinson's Disease Assessment from a Wrist-Worn Wearable Sensor in Free-Living Conditions: Deep Ensemble Learning and Visualization
University of Waterloo（滑铁卢大学）；Ludwig-Maximilians University（路德维希-马克西米利安大学）；Technical University of Munich（慕尼黑工业大学）；Schön Klinik München Schwabing（慕尼黑施瓦宾 Schön 诊所）
AI WALKUP: A Computer-Vision Approach to Quantifying MDS-UPDRS in Parkinson's Disease
Key Lab of Image Processing and Intelligent Control, Ministry of Education（教育部图像处理与智能控制重点实验室）；School of Artificial Intelligence and Automation（人工智能与自动化学院）；Huazhong University of Science and Technology（华中科技大学）
From Visual Attribution to Clinical Reasoning: Explainable Parkinson's Disease Screening from Hand-Drawn Patterns
Indian Institute of Technology Indore（印度理工学院印多尔分校）；Indian Institute of Technology Patna（印度理工学院巴特那分校）；M. R. Bangur Hospital（M. R. 班古尔医院）；University of Western Australia（西澳大利亚大学）
Interpretable and Granular Video-Based Quantification of Motor Characteristics from the Finger Tapping Test in Parkinson Disease
Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center（神经科、帕金森病和运动障碍专家中心、大脑、认知与行为研究所、拉德堡德大学医学中心）；Department of Artificial Intelligence, Donders Institute for Brain, Cognition and Behaviour, Radboud University（人工智能系、大脑、认知与行为研究所、拉德堡德大学）
Interpretable machine learning predicts Parkinson's disease severity using motion-corrected QSM MRI and multiband multiecho fMRI features
Lyda Hill Department of Bioinformatics（Lyda Hill 生物信息学系）；Department of Biomedical Engineering（生物医学工程系）；University of Texas Southwestern Medical Center, Dallas, Texas, USA（德克萨斯西南医学中心，德克萨斯州达拉斯）
Curriculum Based Multi-Task Learning for Parkinson's Disease Detection
University of Southern California（南加州大学）；Chang Gung Memorial Hospital（长庚纪念医院）；University of Pennsylvania Perelman School of Medicine（宾夕法尼亚大学佩雷尔曼医学院）；Chang Gung University（长庚大学）；Amsterdam UMC, Vrije Universiteit Amsterdam（阿姆斯特丹大学医学中心，阿姆斯特丹自由大学）
