将人工智能与手工制作的特征进行比较,以在不同语言中对阿尔茨海默病进行分类
Tong Min Kim1, Junhyeok Son2, Ji-Won Chun3
1Department of Medical Informatics, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea.
Computers in biology and medicine
|August 3, 2024
概括
早期阿尔茨海默病 (AD) 检测是使用AI改进的. 分析语音数据的深度学习模型在不同语言的AD诊断中取得了高准确性,从而实现了实时的,语言不可知测试.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 语音科学 语言科学
背景情况:
- 早期发现阿尔茨海默病 (AD) 仍然是一个重大挑战.
- 阿尔茨海默病患者的言语模式提供了潜在的诊断生物标志物,而不依赖于语言或种族.
- 现有的诊断模型往往缺乏跨语言的适用性.
研究的目的:
- 开发和评估语言不可知的人工智能模型,用于使用语音数据早期检测阿尔茨海默病.
- 为了比较机器学习和深度学习模型在诊断AD的表现从不同语言的语音.
主要方法:
- 从韩国语和英语语音数据集中提取了声学特征.
- 利用十个模型 (四个机器学习,六个深度学习) 来实时分类AD和健康对照.
- 深度学习模型使用了不可解释的,直接获得的语音表示.
主要成果:
- 深度学习模型的准确性比机器学习模型 (高达0.73) 高 (高达0.78).
- 模型在韩国和英语数据集中显示出强度.
- 使用大约30秒的语音样本实现了实时AD诊断,分类时间最小 (0.01-0.02秒).
结论:
- 无法解释的深度学习模型超过了传统的机器学习来诊断AD的语音.
- 开发的AI模型显示出对语言不可知性阿尔茨海默病诊断的前景.
- 这种方法促进了可扩展和高效的早期AD检测.
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