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相关实验视频

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Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
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使用计算机视觉和深度学习技术,对眼睛滴滴的自动化量化.

Christine Conelea1, Hengyue Liang2, Megan DuBois1

  • 1Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA.

Movement disorders : official journal of the Movement Disorder Society
|December 25, 2023
PubMed
概括

计算机视觉使用深度学习准确地检测图雷特综合征 (TS) 患者的眼睛滴答. 这种自动化方法在TS查和治疗监测中提供了一个有前途的定量化工具.

关键词:
图雷特综合征 图雷特综合征青少年 青少年 青少年计算机视觉 计算机视觉机器学习是机器学习.刺痛 (TICS) 是一种动作.

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科学领域:

  • 计算神经科学是一种计算神经科学.
  • 医学成像分析分析 医学成像分析
  • 机器学习在医疗保健中的应用

背景情况:

  • 传统的图雷特综合征 (TS) 量化依赖于主观评分尺度.
  • 现有的客观基于视频的方法资源密集,需要人为评估者.
  • 计算机视觉可以自动检测非典型的运动,以量化.

研究的目的:

  • 应用计算机视觉方法来训练受监督的深度学习算法.
  • 从视频数据中检测TS患者中最常见的眼球,即TS患者中最常见的眼球.

主要方法:

  • 利用了来自11名青少年TS患者的54个视频.
  • 人类评分器确定了1775个眼睛滴滴事件和3680个非滴滴事件.
  • 应用监督深度学习到视频剪辑中的3D面部地标.

主要成果:

  • 通过使用随机分割治疗方案,实现了0.89的曲线下面面积 (AUC) 来对眼睛滴滴进行分类.
  • 脱节分裂疗法的AUC为0.74,表明小患者样本的概括性有限.
  • 该算法在未见的验证数据中成功检测到眼睛的滴答.

结论:

  • 通过计算机视觉自动检测眼球是可行的和准确的.
  • 这项技术显示了改善TS中的 tic量化潜力.
  • 未来的应用包括TS查,诊断和治疗结果评估.