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一个便携和高效的痴呆症查工具,使用眼睛跟踪机器学习和虚拟现实.

Ying Xu1, Chi Zhang2, Baobao Pan2

  • 1Shenzhen Bao'an Centre for Chronic Disease Control, Shenzhen, PR China.

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概括

一个新的VR视觉追踪认知评估 (VECA) 工具使用眼动和人工智能进行早期痴呆查. 这种非侵入性方法可以准确预测认知障碍,为公共卫生提供可扩展的解决方案.

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科学领域:

  • 神经科学是一个神经科学.
  • 人工智能的人工智能
  • 医疗技术 医疗技术 医学技术

背景情况:

  • 痴呆症,特别是阿尔茨海默病,构成了全球卫生挑战.
  • 目前用于临床前痴呆症和轻度认知障碍 (MCI) 的诊断方法通常是侵入性的,昂贵的或不适合大规模查.
  • 眼睛跟踪技术提供了一种非侵入性的方法来检测微妙的认知变化.

研究的目的:

  • 引入和评估VR眼睛追踪认知评估 (VECA) 工具,以进行非侵入性,高效和大规模的痴呆症查.
  • 评估VECA使用眼睛跟踪数据,机器学习和虚拟现实来预测认知障碍的能力.
  • 将VECA的表现与传统的认知评估进行比较.

主要方法:

  • 开发和部署VR眼睛跟踪认知评估 (VECA) 工具,整合眼睛跟踪,机器学习和虚拟现实.
  • 收集了深宝安慢性医院201名参与者的眼睛跟踪数据.
  • 利用支持向量的回归模型来预测蒙特利尔认知评估 (MoCA) 评分,并对认知障碍进行分类.

主要成果:

  • 支持矢量回归模型与MoCA分数实现了高相关性 (0.9),表现优于基线模型.
  • 建立了用于识别认知障碍的最佳切线分数,灵敏度为88.5%,特异性为83%.
  • 在不同教育背景的参与者中证明了VECA的有效性.

结论:

  • 维卡显示出作为早期痴呆症查的便携式和高效工具的巨大潜力.
  • 眼睛跟踪,机器学习和VR的整合为认知健康评估提供了一个有前途的方法.
  • 维卡为大规模认知障碍检测提供了一个可行的,非侵入性的替代方案.