通过多模态认知评估与眼睛跟踪和卷积神经网络分析增强轻度认知障碍辅助识别
Na Li1,2, Ziming Wang2, Wen Ren2
1Shanghai Changning Mental Health Center, Affiliated Mental Health Center of East China Normal University, Shanghai 200335, China.
Biomedicines
|March 28, 2025
概括
这项研究表明,将眼睛跟踪和行为数据与人工智能相结合,可以显著改善轻度认知障碍 (MCI) 的早期检测. 这种多式联络方式为社区查提供了一个有前途的新方法.
科学领域:
- 神经科学是一个神经科学.
- 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:
- 轻度认知障碍 (MCI) 代表了正常衰老和痴呆症之间的关键阶段,需要早期检测以防止认知能力下降.
- 像MMSE和MoCA这样的传统认知评估在早期MCI检测方面存在局限性.
- 这项研究引入了一种新的支持系统,用于使用眼睛跟踪和卷积神经网络 (CNN) 分析来识别MCI.
研究的目的:
- 开发和验证用于早期MCI检测的多式联运辅助识别系统.
- 为了评估眼睛跟踪特征,行为特征及其组合在MCI分类中的有效性.
- 为了比较单一特征模型与综合多式模式模型的性能.
主要方法:
- 使用眼睛追踪技术和机器学习创建了一个多式联网辅助识别模型.
- 128名参与者 (40名MCI,57名老年人对照,31名年轻人) 进行了四次眼动任务和两次认知测试.
- 31 个眼动和 8 个行为特征被提取并使用 CNN 进行分析,以获得分类准确度.
主要成果:
- 与单一特征模型相比,组合特征模型显示出更高的区分精度.
- 多式模式模型在将MCI与健康个体 (包括年轻人) 的区别方面实现了74.62%的平均准确性.
- 将MCI与老年人对照区分开来,结合模型的平均准确率为66.50%.
结论:
- 整合眼睛跟踪和行为数据的多模式模型显著优于MCI识别的单一特征模型.
- 眼睛追踪技术显示了MCI早期检测的巨大潜力.
- 这种方法为提高社区MCI早期检测的有效性提供了一条新的途径.
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