人工智能驱动的预测可能的轻度认知障碍使用眼科认知加法测试 (OCAT)
Gaurav N Pradhan1,2,3, Sarah E Kingsbury1,2, Michael J Cevette1,2
1Aerospace Medicine and Vestibular Research Laboratory, Mayo Clinic, Scottsdale, AZ 85259, USA.
Brain sciences
|January 28, 2026
概括
眼科认知补充测试 (OCAT) 使用眼球运动和时间来创建用于早期轻度认知障碍 (MCI) 检测的AI模型. 这些模型准确地预测认知状态,提供可扩展的查解决方案.
科学领域:
- 眼科医生 眼科 眼科
- 神经学 神经学
- 人工智能的人工智能
- 认知科学 认知科学
背景情况:
- 轻度认知障碍 (MCI) 诊断是具有挑战性的,因为不敏感的简短检查和耗时的详细测试.
- 眼球认知加法测试 (OCAT) 提供了一种快速,客观的方法,在心理任务中使用眼睛跟踪.
- 目前的查工具缺乏广泛早期检测所需的速度和可访问性.
研究的目的:
- 使用OCAT功能开发和验证AI衍生预测模型,用于早期MCI检测.
- 评估眼动和基于时间的OCAT数据在识别患有MCI风险的个人的有效性.
- 建立OCAT作为一个实用,可扩展的认知查工具.
主要方法:
- 250名患者通过集成眼睛追踪完成了OCAT.
- 从凝视数据中提取了与时间相关的和眼睛运动特征.
- 后勤回归 (LR) 和K-近邻 (KNN) 模型使用特征选择 (随机森林,决策树) 进行训练,以预测痴呆症评分表 (DRS) 的结果.
主要成果:
- 使用时间和眼睛运动特征的LR模型实现了高性能 (精度=0.97,回忆=0.91,AUPRC=0.95).
- 仅使用眼睛或时间特征的模型也表现良好 (精度=0.93).
- 组合特征模型在预测DRS结果方面始终优于单一特征集模型.
结论:
- 在OCAT数据上训练的机器学习模型可以可靠地预测认知功能 (DRS PASS/FAIL).
- OCAT显示出作为早期MCI识别的实用和可扩展工具的巨大潜力.
- OCAT系统适用于各种临床环境,包括偏远和资源有限的环境.
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