自动化真实世界的视频分析坐立转变预测帕金森病的严重程度
Catherine Morgan1,2, Alessandro Masullo3, Majid Mirmehdi3
1Translational Health Sciences, University of Bristol, Bristol, UK.
Digital biomarkers
|August 17, 2023
概括
这项研究开发了一种自动方法来跟踪帕金森病 (PD) 患者的坐立 (STS) 过渡,显示它与临床严重程度和药物状态相关联.
科学领域:
- 生物医学工程 生物医学工程
- 神经学 神经学
- 数字健康数字健康
背景情况:
- 帕金森病 (PD) 管理可以从监测疾病进展和治疗反应的技术中受益.
- 坐到站 (STS) 过渡是PD患者的关键日常活动.
研究的目的:
- 开发和验证一种自动化方法,使用现实数据量化STS持续时间和速度.
- 调查这些自动化STS参数与PD严重程度的临床相关性.
- 评估保留PD药物的对STS参数的影响.
主要方法:
- 收集了85小时的视频数据,来自24名参与者在自然环境中.
- 提取了骨关节数据以估计头部轨迹.
- 从头部轨迹数据计算STS持续时间和速度.
主要成果:
- 自动化的STS持续时间和速度与手动测量的参数有显著的相关性.
- 自动化的STS参数与PD严重程度的黄金标准临床评级尺度有很强的相关性.
- 在使用ON药物时,在PD和对照组之间观察到STS持续时间和速度的显著差异.
结论:
- 一种自动化方法来量化STS过渡是可行的,并为PD研究生态有效.
- 自动化的STS参数作为PD严重程度的可靠数字生物标志物.
- 在本研究中,药物状态对单个STS参数的影响有限.
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