Parkinson's disease (PD) is a neuro-degenerative disorder that affects movement, speech, and coordination. Timely diagnosis and treatment can improve the quality of life for PD patients. However, access to clinical diagnosis is limited in low and middle income countries (LMICs). Therefore, development of automated screening tools for PD can have a huge social impact, particularly in the public health sector. In this paper, we present PULSAR, a novel method to screen for PD from webcam-recorded videos of the finger-tapping task from the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS). PULSAR is trained and evaluated on data collected from 382 participants (183 self-reported as PD patients). We used an adaptive graph convolutional neural network to dynamically learn the spatio temporal graph edges specific to the finger-tapping task. We enhanced this idea with a multi stream adaptive convolution model to learn features from different modalities of data critical to detect PD, such as relative location of the finger joints, velocity and acceleration of tapping. As the labels of the videos are self-reported, there could be cases of undiagnosed PD in the non-PD labeled samples. We leveraged the idea of Positive Unlabeled (PU) Learning that does not need labeled negative data. Our experiments show clear benefit of modeling the problem in this way. PULSAR achieved 80.95% accuracy in validation set and a mean accuracy of 71.29% (2.49% standard deviation) in independent test, despite being trained with limited amount of data. This is specially promising as labeled data is scarce in health care sector. We hope PULSAR will make PD screening more accessible to everyone. The proposed techniques could be extended for assessment of other movement disorders, such as ataxia, and Huntington's disease.

翻译：帕金森病（PD）是一种影响运动、言语和协调能力的神经退行性疾病。及时诊断和治疗可提高PD患者的生活质量。然而，在中低收入国家（LMICs），临床诊断的可及性有限。因此，开发PD自动化筛查工具可产生巨大的社会影响，尤其是在公共卫生领域。本文提出PULSAR，一种新颖的方法，用于从网络摄像头录制的基于运动障碍学会-统一帕金森病评定量表（MDS-UPDRS）的指扣任务视频中筛查PD。PULSAR在382名参与者（其中183名自我报告为PD患者）收集的数据上进行训练和评估。我们采用自适应图卷积神经网络动态学习特定于指扣任务的时空图边。通过多流自适应卷积模型增强这一思想，以从对检测PD至关重要的不同数据模态（例如指关节相对位置、敲击速度和加速度）中学习特征。由于视频标签为自我报告，非PD标签样本中可能存在未确诊的PD病例。我们利用正无标签（PU）学习的思想，该方法无需标注负样本。实验表明，以这种方式建模问题具有明显优势。尽管训练数据有限，PULSAR在验证集上达到80.95%的准确率，在独立测试集中平均准确率为71.29%（标准差2.49%）。这一成果在医疗领域标注数据稀缺的背景下尤为令人鼓舞。我们希望PULSAR能使PD筛查惠及更多人。所提出的技术可推广用于其他运动障碍的评估，如共济失调和亨廷顿病。