生物标志物 生物标志物
James Glass1, Liming Wang1, Saurabhchand Bhati1
1Massachusetts Institute of Technology, Cambridge, MA, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 24, 2025
概括
新的数字语音分析工具可以通过从语音中提取关键特征来帮助检测早期阿尔茨海默病 (AD) 和相关痴呆症 (ADRD). 这些开源工具解决了隐私问题,并使得使用语音生物标志物对临床前AD/ADRD检测进行更广泛的研究.
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
- 生物医学工程 生物医学工程
背景情况:
- 数字语音 (dVoice) 分析是一种新兴的,可访问的方法,用于早期检测阿尔茨海默病 (AD) 和相关痴呆症 (ADRD).
- 挑战包括在语音数据中保护个人识别信息 (PII) 和需要自动化功能提取工具.
- 全球研究和成像平台 (GRIP) 正在开发开源的dVoice处理工具,以克服这些障碍.
研究的目的:
- 开发和验证一套开源工具,用于分析数字语音录音,以便早期检测AD/ADRD.
- 通过实施掩盖语音打印和从转录中删除个人信息的方法来解决隐私问题.
- 为了使人工智能驱动的声音特征的分析表明认知和行为症状.
主要方法:
- 利用了来自弗雷明翰心脏研究 (FHS) 的超过33,000个纵向语音录音.
- 开发了一种虚构的语音转换 (FVC) 方法,在保留音频特征的同时掩盖语音打印.
- 创建了用于音频,语言和双语特征提取的自动化工具,以及用于删除PII的NLP框架和保护隐私的AI分析管道.
主要成果:
- 应用FVC,特征提取,NLP-PII和保护隐私的AI分析到各种数据集,包括FHS,ADReSS和DementiaBank.
- 成功地从FHS dVoice录音中提取了声学,语言和语言特征.
- 与原生语音录音相比,FVC方法在保持分析可比性方面存在局限性.
结论:
- GRIP版本1 dVoice处理工具对于分析不同准确度的录音非常强大.
- 这些工具促进了对语音生物标志物的研究,以测量早期和临床前ADRD的认知和行为症状.
- 开源版本使得数字语音分析在神经退行性疾病检测领域的广泛采用和进一步发展成为可能.
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