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患者的运动表现。参与者在网络摄像头前进行了一个运动任务(即点击手指),全球250名参与者的数据由三名专家神经科医生根据运动疾病协会统一帕金森病评分量表(MDS-UPDRS)进行评分。神经科医生的评分高度可靠,内部一致性系数(ICC)为0.88。我们开发了计算机算法,以获得与MDS-UPDRS指南相一致的客观测量,并与神经科医生的评分强相关。我们的机器学习模型训练了这些度量标准,表现优于MDS-UPDRS认证的评分员,平均绝对误差(MAE)为0.59,而评分员的MAE为0.79。但是,与专家神经科医生相比,该模型的表现略差(0.53 MAE)。该方法可复制用于类似的运动任务,为在有限的神经科护理区域远程、客观评估帕金森病患者及其他运动障碍提供可能。