生物标志物 生物标志物
Jelena Curcic1, Iris Nowenstein2, Bryndís Bergþórsdóttir2
1Novartis Biomedical Research, Basel, Switzerland.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 24, 2025
概括
这项研究探讨了用于早期发现阿尔茨海默病的自动语音和语言分析. 结果将有助于验证这些工具的客观,非侵入性患者监测.
科学领域:
- 神经学 神经学
- 计算语言学 计算语言学
- 老年学是指老年学的学科.
背景情况:
- 语言技术的进步为早期发现和监测阿尔茨海默病 (AD) 提供了潜力.
- 自动语音和语言特征的临床解释性需要根据患者的结果进行评估.
- 研究必须扩展到英语之外,以确保平等地获得基于语言的健康技术.
研究的目的:
- 调查自动提取的语音和语言特征的临床可解释性.
- 将这些特征与以患者为中心的结果联系起来,比如说话流性和查词困难.
- 为阿尔茨海默病研究评估冰岛语音者的自动语音和语言分析.
主要方法:
- 48名以冰岛语为母语的人被分为四组:认知健康 (粉样阴性/阳性),轻度认知障碍和轻度阿尔茨海默病.
- 参与者完成了在三个会议中记录的口头图片描述任务.
- 声学和词汇特征被自动提取,以及两名语言语言病理学家的专家评分.
主要成果:
- 目前正在进行分析,重点是语音语言病理学家之间的评审者间可靠性.
- 将介绍自动化功能与专家评级之间的相关性.
- 将比较使用这两种方法的队列分类准确性.
结论:
- 自动语音和语言分析为阿尔茨海默病的主观和繁重的临床试验终点提供了一个有希望的替代方案.
- 这些客观,非侵入性评估针对AD的核心临床特征.
- 这项研究促进了全球临床试验的基于语言的健康技术的发展.
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