Michael H Li1, Tiago A Mestre2, Susan H Fox3, Babak Taati4. 1. Toronto Rehabilitation Institute, University Health Network, 550 University Ave, Toronto, Ontario, M5G 2A2, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College St, Room 407, Toronto, Ontario, M5S 3G9, Canada. Electronic address: michaelhg.li@alum.utoronto.ca. 2. Edmond J. Safra Program in Parkinson's Disease, Toronto Western Hospital, University Health Network, 399 Bathurst St, Toronto, Ontario, M5T 2S8, Canada; The Ottawa Hospital Research Institute, 1053 Carling Ave, Ottawa, Ontario, K1Y 4E9, Canada; Division of Neurology, Department of Medicine, 1053 Carling Ave, Ottawa, Ontario, K1Y 4E9, Canada; Division of Neurology, University of Toronto, Suite RFE 3-805, 200 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada. Electronic address: tmestre@toh.ca. 3. Edmond J. Safra Program in Parkinson's Disease, Toronto Western Hospital, University Health Network, 399 Bathurst St, Toronto, Ontario, M5T 2S8, Canada; Division of Neurology, University of Toronto, Suite RFE 3-805, 200 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada. Electronic address: susan.fox@uhnresearch.ca. 4. Toronto Rehabilitation Institute, University Health Network, 550 University Ave, Toronto, Ontario, M5G 2A2, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College St, Room 407, Toronto, Ontario, M5S 3G9, Canada; Department of Computer Science, University of Toronto, 10 King's College Road, Room 3302, Toronto, Ontario, M5S 3G4, Canada. Electronic address: babak.taati@uhn.ca.
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.
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.
Authors: Jan Stenum; Kendra M Cherry-Allen; Connor O Pyles; Rachel D Reetzke; Michael F Vignos; Ryan T Roemmich Journal: Sensors (Basel) Date: 2021-11-03 Impact factor: 3.576