基于变压器的深度学习方法用于基于语音的痴呆症检测:系统性审查
IEEE journal of biomedical and health informatics
|August 6, 2025
概括
使用患者语音进行深度学习,显示了早期痴呆症检测的前景. 分析语言特征的变压器模型达到高精度,指导未来的认知障碍诊断研究.
科学领域:
- 人工智能的人工智能
- 神经科学是一个神经科学.
- 老年学是一门学科.
背景情况:
- 越来越多的老年人群需要具有成本效益的早期痴呆症检测方法.
- 分析语音样本的深度学习模型为痴呆症诊断提供了一个有希望的途径.
研究的目的:
- 系统地审查关于用于痴呆症诊断的基于语音的深度学习的研究.
- 确定未来数据驱动性痴呆症研究和临床决策支持的最佳实践.
主要方法:
- 对80项研究进行了系统审查,这些研究来源于主要的科学数据库.
- 分析的重点是模型架构,性能,语音特征 (语言与声学) 和数据集.
- 研究了基于变压器的模型和语言特征的有效性.
主要成果:
- 基于变压器的深度学习模型是最常见的,平均准确率为85.71%.
- 在痴呆症检测中,与声学特征相比,语言语音特征表现优越.
- 确定的局限性包括数据集多样性,严重程度分类不一致,以及性能指标的可变报告.
结论:
- 变压器模型显示了增强基于语音的深度学习在认知障碍检测中的潜力.
- 解决数据集多样性和报告标准的局限性对于可重现性和临床翻译至关重要.
- 未来的研究应该专注于标准化的方法,以推进痴呆症的诊断决策支持系统.
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