  Objective: To apply deep learning pose estimation algorithms for vision-based
assessment of parkinsonism and levodopa-induced dyskinesia (LID). Methods: Nine
participants with Parkinson's disease (PD) and LID completed a levodopa
infusion protocol, where symptoms were assessed at regular intervals using the
Unified Dyskinesia Rating Scale (UDysRS) and Unified Parkinson's Disease Rating
Scale (UPDRS). A state-of-the-art deep learning pose estimation method was used
to extract movement trajectories from videos of PD assessments. Features of the
movement trajectories were used to detect and estimate the severity of
parkinsonism and LID using random forest. Communication and drinking tasks were
used to assess LID, while leg agility and toe tapping tasks were used to assess
parkinsonism. Feature sets from tasks were also combined to predict total
UDysRS and UPDRS Part III scores. Results: For LID, the communication task
yielded the best results for dyskinesia (severity estimation: r = 0.661,
detection: AUC = 0.930). For parkinsonism, leg agility had better results for
severity estimation (r = 0.618), while toe tapping was better for detection
(AUC = 0.773). UDysRS and UPDRS Part III scores were predicted with r = 0.741
and 0.530, respectively. Conclusion: This paper presents the first application
of deep learning for vision-based assessment of parkinsonism and LID and
demonstrates promising performance for the future translation of deep learning
to PD clinical practices. Significance: The proposed system provides insight
into the potential of computer vision and deep learning for clinical
application in PD.
