从视频中深度学习帕金森病的运动,没有人类定义的措施
Jiacheng Yang1, Stefan Williams2, David C Hogg1
1School of Computing, University of Leeds, UK.
Journal of the neurological sciences
|July 11, 2024
概括
一个深度学习模型可以只使用手指敲击视频准确地分类帕金森病 (PD). 这种人工智能方法消除了对专家分析或预定义特征的需求,为PD提供了一个新的诊断工具.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 帕金森病 (PD) 诊断依赖于布拉迪基尼西亚,通常通过手指敲击来评估.
- 目前的手指敲击评估需要专家的观察,这很少,容易变化.
- 现有的技术方法使用有限的,研究人员定义的特征从窃听信号.
研究的目的:
- 直接将深度学习神经网络应用于指纹点击视频,以进行PD分类.
- 评估AI在区分异态PD与对照病人的准确性.
- 想象深度学习模型的学习特征.
主要方法:
- 从40名PD患者和37名对照组中收集了152个手机视频,记录了40名PD患者和37名对照组的手指敲击.
- 通过向下采样和分割成1秒钟的剪辑来处理视频.
- 在视频片段上训练了一个3D卷积神经网络.
主要成果:
- 深度学习模型实现了0.69的测试准确度来区分PD与对照.
- 该模型显示测试精度为0.73和测试回忆率为0.76.
- 类激活地图识别出了独特的空间和时间特征,包括PD患者独特的指运动.
结论:
- 深度学习可以直接分析手指点击视频,以区分PD与控制.
- 这种人工智能方法绕过了手动特征提取或专家解释的需要.
- 这项研究提出了一种新的,以技术为导向的PD评估方法.
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