生物标志物 生物标志物
Sunghye Cho1, Taehwan Kim2, Sung-Woo Kim3
1Linguistic Data Consortium, University of Pennsylvania, Philadelphia, PA, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 26, 2025
概括
这项研究表明,结合语音文本和声学特征,可以准确地区分阿尔茨海默病 (AD) 与患有莱维体 (DLB) 痴呆症. 这种自动化方法有助于为临床试验进行大规模的患者查.
科学领域:
- 神经学 神经学
- 计算语言学 计算语言学
- 生物医学工程 生物医学工程
背景情况:
- 阿尔茨海默氏症 (AD) 和患有莱维体 (DLB) 的痴呆症有着共同的早期症状,如记忆和认知问题.
- 在大规模的患者查中,区分AD与DLB是具有挑战性的.
- 建议使用语音特征进行自动分类,以区分AD和DLB.
研究的目的:
- 开发和评估自动分类系统,以区分阿尔茨海默病 (AD) 和患有莱维体痴呆症 (DLB).
- 调查结合文本和声学语音特征用于差异诊断的有效性.
- 为了促进可扩展的临床试验和干预患者的查.
主要方法:
- 从患有AD (ADCI) 和DLB (DLBCI) 的患者和健康对照组 (HC) 收集的语音数据.
- 使用大型语言模型 (KLUE-RoBERTa-base) 提取的文本嵌入和使用OpenSMILE的声学特征.
- 应用主要组件分析以减少特征,并对多层感知子分类器的数据级和决策级融合进行了实验.
主要成果:
- 合并模型 (文本 + 声学特征) 在区分HC与所有患者方面实现了75.7%的准确性 (AUC=0.77).
- 对于ADCI与DLBCI分类,决策级融合产生了最高的性能,准确度为80.3% (AUC=0.82).
- 融合模型的表现优于纯文本或纯声学方法.
结论:
- 使用语音分析对ADCI和DLBCI进行自动分类是一个有前途的工具.
- 这种方法可以在临床试验中对患者进行大规模查方面发挥重要作用.
- 基于语音的分析为差异诊断提供了一种可行的非侵入性方法.
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