临床表现 临床表现
Gonzalo Nicolás Pérez1,2,3, Ivan Caro1,3, Joaquín Valdez Bisé4
1Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 25, 2025
概括
自动语音分析准确地预测了西班牙语拉丁人的认知衰退和大脑变化. 这种负担得起的方法有助于早期检测神经认知障碍,如阿尔茨海默氏症和痴呆症.
科学领域:
- 神经科学是一个神经科学.
- 计算语言学 计算语言学
- 老年学是一门学科.
背景情况:
- 老龄化人口面临神经认知障碍的增加,包括主观认知衰退 (SCD),轻度认知障碍 (MCI) 和阿尔茨海默病痴呆症 (ADD).
- 传统的诊断方法 (认知测试,神经影像) 是昂贵的,耗时的,在许多地区都无法使用.
- 自动语音和语言分析 (ASLA) 为服务不足社区的早期检测提供了一个具有成本效益的,可扩展的替代方案.
研究的目的:
- 研究ASLA在智利西班牙语人士的神经认知衰退连续体中预测认知和神经成像测量的有效性.
- 解决ASLA研究中针对这个特定的人口和认知范围的差距.
主要方法:
- 150名智利参与者 (17个对照组,55个SCD,57个MCI,21个ADD) 完成了流利性任务,认知评估 (ACE-III,MoCA,IFS) 和MRI扫描.
- 机器学习模型使用TELL应用程序从流利性任务中分析了语音特征 (单词属性,时间).
- 模型预测了认知测试成绩和大脑体积 (灰质,白质,海马).
主要成果:
- 在ASLA衍生的特征和认知得分之间发现了显著的相关性 (ACE-III: r=0.55,MoCA: r=0.39,IFS: r=0.31).
- 此外,ASLA与正常化灰质体积 (r=0.34) 也相关.
- 这些发现是最强大的使用单词属性特征从结合流利性任务.
结论:
- 一个完全自动化的ASLA管道有效地从简短的语言任务中捕获大脑健康的认知和MRI指标.
- 这种方法独立于检查者,客观,时间效率高,价格合理,比标准诊断工具更有优势.
- 阿斯拉代表了一种可扩展的解决方案,以促进全球健康公平,打击痴呆症和认知衰退.
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