  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.
