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Automated Vision-Based Analysis of Levodopa-Induced Dyskinesia with Deep Learning

文献类型： 会议论文

第一作者： Michael H. Li

作者： Michael H. Li 1 ; Tiago A. Mestre 2 ; Susan H. Fox 3 ; Babak Taati 4 ;

作者机构： 1.Institute of Biomaterials and Biomedical Engineering, University of Toronto

2.The Ottawa Hospital Research Institute, uOttawa Brain and Mind Research Institute and Department of Medicine, University of Ottawa

3.TWH-UHN

4.TRI-UHN

会议名称： Annual International Conference of the IEEE Engineering in Medicine and Biology Society

主办单位：

页码： 3377-3380

摘要： Levodopa is the gold standard therapy for Parkinson's disease (PD), but its prolonged usage leads to additional motor complications, namely levodopa-induced dyskinesia (LID). To assess LID and adjust drug regimens for optimal relief, patients attend regular clinic visits. However, the intermittent nature of these visits can fail to capture important changes in a person's condition. With the recent emergence of deep learning achieving impressive results in a wide array of fields including computer vision, there is an opportunity for video analysis to be used for automated assessment of LID. Deep learning for pose estimation was studied as a viable means of extracting body movements from PD assessment videos. Movement features were computed from joint trajectories. Results show that features derived from vision-based analysis have moderate to good correlation with clinician ratings of dyskinesia severity. This study presents the first application of deep learning to video analysis in PD, and demonstrates promise for future development of a non-contact system for objective PD assessment.

分类号： Q81-53

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