使用微笑和与聊天机器人对话的阿尔茨海默病数字检测
Haruka Takeshige-Amano1, Genko Oyama2, Mayuko Ogawa1
1Department of Neurology, Faculty of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan.
Scientific reports
|November 2, 2024
概括
这项研究开发了一个人工智能聊天机器人,使用面部和语音暗示来选阿尔茨海默病 (AD). 聊天机器人有效地将阿尔茨海默病患者与健康对照者区分开来,提供了一个实用的认知评估工具.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 生物标志物 生物标志物
背景情况:
- 痴呆症,特别是阿尔茨海默病 (AD),在超级老年社会中构成了重大挑战.
- 在各个领域,有效和易于使用的认知评估工具至关重要.
- 面部和语音特征显示出作为痴呆症检测成本效益高的生物标志物具有前途.
研究的目的:
- 使用聊天机器人和人工智能开发一种简单,自动和广泛的阿尔茨海默病 (AD) 查工具.
- 为了评估AD查,从聊天机器人对话中结合面部和听觉数据的有效性.
主要方法:
- 机器学习分析了99名健康对照 (HCs) 和93名患有AD或AD (PwA) 轻度认知障碍的人的微笑图像和自然对话数据 (视觉和听觉).
- 特征提取侧重于8个面部和21个声音特征用于分类,以及8个面部和20个声音特征用于认知得分预测.
主要成果:
- 一个子集的特征在区分PWA和HC时实现了高精度 (AUC为0.94±0.05).
- 该模型准确地预测了认知测试得分,平均绝对误差低 (5.78 ± 0.08).
- 性能超越了仅使用面部或听觉数据或常规图像任务的方法.
结论:
- 从聊天机器人交互中结合自发的面部和声音数据,为AD查提供了一个强大的工具.
- 拟议的AI驱动模型证明了对现实生活认知状态评估的实际适用性.
- 这种方法为早期发现和监测认知衰退提供了一种新的非侵入性方法.
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