临床表现 临床表现
Alice Stanton1, Claire L Lancaster2, Jennifer M Rusted3
1University of Sussex, Brighton, East Sussex, United Kingdom.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 25, 2025
概括
新的口语流利任务 (VFT) 评分方法,手动和自动,可以检测早期的神经认知变化被传统评估遗漏. 这些先进的技术可以改善早期痴呆风险的识别.
科学领域:
- 神经科学是一个神经科学.
- 认知心理学 认知心理学
- 计算语言学 计算语言学
背景情况:
- 早期发现像阿尔茨海默氏症这样的神经退行性疾病对于及时干预至关重要.
- 传统的口语流利任务 (VFT) 评分可能无法捕捉微妙的临床前认知变化.
- 先进的VFT评分方法为提升痴呆风险的早期检测提供了潜力.
研究的目的:
- 系统地审查和评估细微的手动和自动化的口语流利任务 (VFT) 评分方法.
- 评估先进的VFT指标 (例如,聚类,切换) 的实用性,以识别有未来痴呆风险的个人.
- 综合关于新型VFT评分在临床前神经认知评估中的有效性的发现.
主要方法:
- 按照PRISMA指南,对PubMed,PsycINFO和Scopus数据库进行系统的文献搜索 (开始至2024年9月).
- 纳入标准:在患有阿尔茨海默病,轻度认知障碍,生物标志物阳性或APOE4变异的成年人中进行自动或非传统的手动VFT评分.
- 排除标准:仅使用传统VFT评分或声学语音特征的研究;研究结果的叙事综合.
主要成果:
- 包括53项研究,其中41.5%使用自动化和58.5%使用新手工评分方法.
- 先进的语义聚类 (例如,Word2Vec) 和语言特征 (单词频率,典型性) 改善了早期认知障碍的检测和诊断准确度.
- 纵向数据表明,自动集群在9年内增强了痴呆症的预测能力;增加的单词典型性与MCI与AD过渡风险相关.
结论:
- 无论是自动化还是手动的VFT分析,都显示出对检测早期认知衰退的重大前景.
- 自动化方法提供速度和一致性;手动方法提供解释性和灵活性.
- 未来的研究应该优先开发透明,验证和临床集成的自动化VFT工具,通过共同设计为多样化的群体.
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