痴呆症和MCI检测基于从视频中采集的对话中的面部表情综合分析
IEEE journal of biomedical and health informatics
|March 3, 2025
概括
这项研究引入了一种新的数字生物标志物,使用面部表情来检测痴呆症和轻度认知障碍 (MCI). 该方法显示了高精度,为早期疾病查提供了具有成本效益的工具.
科学领域:
- 神经学 神经学
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
背景情况:
- 早期发现痴呆症和轻度认知障碍 (MCI) 是至关重要的,但具有挑战性.
- 现有的数字生物标志物通常依赖于语音或语言分析,面部表情分析研究有限.
- 需要具有成本效益和可解释的神经退行性疾病查工具.
研究的目的:
- 开发和验证一种新的方法来检测痴呆和MCI使用视频录制的面部表情特征.
- 探索特定的面部表情特征作为认知衰退的数字生物标志物的实用性.
- 评估机器学习模型在根据这些特征对痴呆症和MCI进行分类方面的表现.
主要方法:
- 从视频中提取四种类型的面部表情特征:动作单元,情感类别,Valence-Arousal和面部嵌入.
- 统计特征工程从提取的面部数据中推导出预测变量.
- 使用基于决策树的模型来对痴呆和MCI进行分类.
- 在对话任务中使用面部视频录像进行评估.
主要成果:
- 实现了痴呆症检测的0.933的曲线下面面积 (AUC) 和MCI检测的0.889.
- 在健康参与者和痴呆症患者之间的面部表情中发现了显著的差异,包括较少的积极情绪和较低的价值/唤起.
- 提出的方法在区分认知障碍阶段方面表现强.
结论:
- 面部表情分析可以作为一种可行的数字生物标志物来检测痴呆症和MCI.
- 拟议的方法是可解释的,并为早期查认知衰退提供了一种具有成本效益的方法.
- 对面部动态的进一步研究可以提高神经退行性疾病的早期诊断.
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