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Updated: Jun 11, 2025

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使用多个实例学习的声学COVID-19检测.

Michael Reiter, Franz Pernkopf

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    一个机器学习工具分析咳和语音以检测COVID-19,提供了一个低成本,快速的诊断替代方案. 这种方法通过整合各种音频信号和患者数据,实现了92.2%的准确性.

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    科学领域:

    • 人工智能的人工智能
    • 生物医学工程 生物医学工程
    • 计算生物学 计算生物学

    背景情况:

    • 由于COVID-19的流行,需要快速和广泛的诊断测试.
    • 传统的测试方法可能耗时且昂贵,限制了可访问性.
    • 机器学习 (ML) 为低成本,可扩展的基于音频的诊断提供了一个潜在的解决方案.

    研究的目的:

    • 开发和评估基于ML的诊断工具,用于使用音频记录检测COVID-19.
    • 通过DiCOVA挑战建立ML算法之间的可比性.
    • 通过结合多种音频模式和患者元数据来提高诊断准确度.

    主要方法:

    • 使用了Coswara数据集,包括咳,言语,呼吸和母音发音记录.
    • 采用在短音频间隔上预训练的基本ML模型.
    • 实施了多个实例学习 (MIL) 模型,用于分析更长的记录.
    • 应用了线性回归和其他融合方法,将DiCOVA挑战的融合类别的不同声音模式的预测结合起来.

    主要成果:

    • MIL方法提高了模型的通用性,在聚变类别中获得AUC ROC得分为86.6%.
    • 结合持续的元音发音和患者元数据显著改善了性能.
    • 最终的模型实现了92.2%的诊断准确度得分.

    结论:

    • 基于音频的ML分析是COVID-19查的一种有希望的,具有成本效益的方法.
    • MIL方法有效地利用更长的音频录音来提高诊断准确度.
    • 整合各种数据源,包括新的声音模式和元数据,进一步提高了ML诊断性能.