在FTD表型中使用自然语音对AD病理的自动分类
Sunghye Cho1, Christopher A Olm2, Sharon Ash2
1Linguistic Data Consortium, Department of Linguistics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
概括
语音分析可以在前性痴呆症 (FTD) 患者中识别阿尔茨海默病的神经病理变化 (ADNC). 这项技术有助于早期诊断,并支持FTD数字查工具的开发.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 语言病理学 语音病理学
背景情况:
- 在像前性痴呆症 (FTD) 这样的非典型表现的个体中,查阿尔茨海默病神经病理变化 (ADNC) 是临床上具有挑战性的.
- 将ADNC与其他病理区分开来,例如前叶退化 (FTLD),对于有效的患者管理至关重要.
研究的目的:
- 开发和验证基于语音的自动分类器,以区分患有ADNC的FTD患者和患有FTLD的FTD患者.
- 识别与不同病理相关的语音特征及其相应的神经解剖学相关物.
主要方法:
- 训练有素的自动分类器使用来自179名参与者的1分钟语音样本的99个语音特征 (36个ADNC,60个FTLD,89个健康对照).
- 根据尸检或脑脊液 (CSF) 生物标志物分配的神经病理学.
- 利用基于结构网络的磁共振成像 (MRI) 来分析语音特征的解剖相关性.
主要成果:
- 分类器实现了0.88 ± 0.03的曲线下的面积 (AUC),以区分ADNC和FTLD.
- 该分类器实现了0.93±0.04的AUC,用于区分患者和健康对照.
- 名词频率和暂停率与灰色物质体积损失相关,分别在边缘和突出网络中.
结论:
- 简短的,自然的语音样本是有效的在体内查FTD患者的底层ADNC.
- 这项研究支持FTD数字评估工具的开发,改善诊断能力.
- 这些发现突显了语音分析与人工智能结合用于非侵入性神经病理学查的潜力.
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