Automated assessment of levodopa-induced dyskinesia: Evaluating the responsiveness of video-based features. (August 2018)

• Record Type: Journal Article
• Authors: Li, Michael H.; Mestre, Tiago A.; Fox, Susan H.; Taati, Babak
• Is Part Of: Parkinsonism & related disorders. Volume 53(2018)
• Page Start: 42
• Page End: 45
• Publication Date: 2018-08
• Subjects: Parkinson's disease -- Levodopa-induced dyskinesia -- Computer vision -- Clinimetric testing -- Objective assessment; Parkinson's disease -- Periodicals; Movement disorders -- Periodicals; Movement Disorders -- Periodicals; Nerve Degeneration -- Periodicals; Nervous System Diseases -- Periodicals; Parkinson Disease -- Periodicals; Tremor -- Periodicals; Parkinson, Maladie de -- Périodiques; Parkinson's disease; 616.833
• DOI: 10.1016/j.parkreldis.2018.04.036
• Languages: English
• ISSNs: 1353-8020

Abstract

Abstract: Introduction: Technological solutions for quantifying Parkinson's disease (PD) symptoms may provide an objective means to track response to treatment, including side effects such as levodopa-induced dyskinesia. Vision-based systems are advantageous as they do not require physical contact with the body and have minimal instrumentation compared to wearables. We have developed a vision-based system to quantify a change in dyskinesia as reported by patients using 2D videos of clinical assessments during acute levodopa infusions. Methods: Nine participants with PD completed a total of 16 levodopa infusions, where they were asked to report important changes in dyskinesia (i.e. onset and remission). Participants were simultaneously rated using the UDysRS Part III (from video recordings analyzed post-hoc). Body joint positions and movements were tracked using a state-of-the-art deep learning pose estimation algorithm applied to the videos. 416 features (e.g. kinematics, frequency distribution) were extracted to characterize movements. The sensitivity and specificity of each feature to patient-reported changes in dyskinesia severity was computed and compared with physician-rated results. Results: Features achieved similar or superior performance to the UDysRS for detecting the onset and remission of dyskinesia. The best AUC for detecting onset of dyskinesia was 0.822 and for remission of dyskinesia was 0.958, compared to 0.826 and 0.802 for the UDysRS. Conclusions: Video-based features may provide an objective means of quantifying the severity of levodopa-induced dyskinesia, and have responsiveness as good or better than the clinically-rated UDysRS. The results demonstrate encouraging evidence for future integration of video-based technology into clinical research and eventually clinical practice. Highlights: We propose a video-based system to objectively assess levodopa-induced dyskinesia. Features were extracted using a deep learning model for human pose estimation. Features show good responsiveness to patient-reported changes compared to UDysRS.