We present an artificial intelligence system to remotely assess the motor performance of individuals with Parkinson's disease (PD). Participants performed a motor task (i.e., tapping fingers) in front of a webcam, and data from 250 global participants were rated by three expert neurologists following the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The neurologists' ratings were highly reliable, with an intra-class correlation coefficient (ICC) of 0.88. We developed computer algorithms to obtain objective measurements that align with the MDS-UPDRS guideline and are strongly correlated with the neurologists' ratings. Our machine learning model trained on these measures outperformed an MDS-UPDRS certified rater, with a mean absolute error (MAE) of 0.59 compared to the rater's MAE of 0.79. However, the model performed slightly worse than the expert neurologists (0.53 MAE). The methodology can be replicated for similar motor tasks, providing the possibility of evaluating individuals with PD and other movement disorders remotely, objectively, and in areas with limited access to neurological care.

翻译：我们提出了一种人工智能系统，用于远程评估帕金森病（PD）患者的运动功能表现。参与者在网络摄像头前完成一项运动任务（即手指敲击），由三位神经病学专家根据运动障碍协会统一帕金森病评定量表（MDS-UPDRS）对来自250名全球参与者的数据进行评分。神经病学家的评分具有高度可靠性，组内相关系数（ICC）为0.88。我们开发了计算机算法，以获取符合MDS-UPDRS指南的客观测量指标，这些指标与神经病学家的评分高度相关。基于这些指标训练的机器学习模型优于通过MDS-UPDRS认证的评分员，其平均绝对误差（MAE）为0.59，而评分员的MAE为0.79。然而，该模型的表现略逊于神经病学专家（MAE为0.53）。该方法可推广至类似的运动任务，为在神经病学护理受限地区对帕金森病及其他运动障碍患者进行远程、客观评估提供了可能性。