Movement Disorders Analysis Using a Deep Learning Approach
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 50
- 试验地点
- 1
- 主要终点
- Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III score
研究概览
简要总结
Bradykinesia is a key parkinsonian feature yet subjectively assessed by the MDS-UPDRS score, making reproducible measurements and follow-up challenging.
In a Movement Disorder Unit, the investigators acquired a large database of videos showing parkinsonian patients performing Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III protocols.
Using a Deep Learning approach on these videos, the investigators aimed to develop a tool to compute an objective score of bradykinesia from the three upper limb tests described in the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Age > 18 years
排除标准
- •Refusal of participation
结局指标
主要结局
Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III score
时间窗: 1 day
Automatic Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III for hand bradykinesia including the three following specific tasks (finger tapping, hand movements and pronation-supination movements of hand). The minimum score is 0. The maximum score is 12 Higher scores mean wors outcome
次要结局
未报告次要终点
研究者
Desjardins Clement
Neurology Resident
Hospital Avicenne