机器学习方法通过语音和步态分析检测痴呆症:一个系统的文献审查
Mustafa Al-Hammadi1, Hasan Fleyeh1, Anna Cristina Åberg2,3
1School of Information and Engineering, Dalarna University, Falun, Sweden.
Journal of Alzheimer's disease : JAD
|June 7, 2024
概括
机器学习,特别是使用步态和语音,对早期痴呆症检测有希望. 非侵入性方法为患者提供了不那么繁重的方法,为自动查铺平了道路.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 痴呆症包括进展性神经退行性疾病,如阿尔茨海默病.
- 早期检测对于及时干预和管理至关重要.
- 人工智能 (AI) 提供了机器学习 (ML) 辅助早期检测的潜力.
研究的目的:
- 审查基于ML的最先进的痴呆症预测方法.
- 专注于非侵入性方法,如步态和语音分析,以进行具有成本效益的查.
- 评估这些方法对早期认知健康见解的潜力.
主要方法:
- 按照PRISMA协议进行的系统文献审查.
- 在Scopus,科学网和PubMed数据库中进行了搜索 (2017-2022年).
- 选择并审查了40项相关研究.
主要成果:
- 支持矢量机器和深度学习是常见的ML方法.
- 多模式方法显示出优越的预测性能.
- 步态分析中的深度学习正在出现;全身运动特征提高了准确性.
- 结合声学,语言和认知特征的语音分析产生了更好的结果.
结论:
- ML,特别是非侵入性方法,显示出早期痴呆症预测的巨大潜力.
- 手动和自动语音分析的可比准确性表明未来的自动检测.
- 非侵入性步行和语音分析对具有成本效益的痴呆症查有希望.
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