利用自然驾驶数字生物标志物进行早期轻度认知障碍检测:深度学习策略
Firas Al-Hindawi1,2, Teresa Wu3,4, Yutong Wen3,4
1Industrial & Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.
JMIR medical informatics
|March 6, 2026
概括
驾驶模式可以早期检测轻度认知障碍 (MCI). 分析自然驾驶数据的深度学习模型显示,对非侵入性认知查和痴呆症预防有前途.
科学领域:
- 神经学 神经学
- 数字健康数字健康
- 机器学习 机器学习
背景情况:
- 阿尔茨海默病和相关的痴呆症是全球日益严重的健康问题.
- 早期发现轻度认知障碍 (MCI) 对于及时干预至关重要.
- 自然驾驶行为为认知评估提供了一个有希望的,非侵入性的数字生物标志物.
研究的目的:
- 利用自然驾驶数据开发深度学习策略,用于早期MCI检测.
- 克服先前研究的局限性,依赖于受控设置或简化数据.
- 建立驾驶行为作为认知衰退的数字生物标志物.
主要方法:
- 从参与者自然驾驶中收集的车载传感器数据 (GPS,加速度计,陀螺仪).
- 将数据细分成完整的行程和转向机动作进行分析.
- 我们比较了三种深度学习建模策略:单视图,特征级融合和模型级迟融合.
主要成果:
- 使用全程数据的模型实现了78%的准确性和77%的AUC,超过了单轮分析.
- 转向和行程数据的晚期融合改善了性能,但没有超过全行程基线.
- 随着更多的数据,分类的准确性增加了,旅行智能的建模更好地捕捉了MCI的偶发性质.
结论:
- 自然驾驶行为是一种可扩展的,非侵入性的早期认知查方法.
- 分析全程驾驶数据的深度学习模型显示了MCI检测的潜力.
- 该框架支持现实世界的监测和数字健康干预措施,以预防痴呆症.
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