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一个新的机器学习模型可以仅使用音频样本来检测喉功能障碍 (VPD). 这项技术可以显著改善患有VPD的个体的诊断机会,特别是在资源较少的环境中.

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科学领域:

  • 语音语言病理学 语言病理学
  • 医疗保健中的人工智能
  • 医学诊断 医学诊断 医学诊断

背景情况:

  • 脊髓功能障碍 (VPD) 影响到30%的患者后palatoplasty,估计主要来自高收入国家.
  • 在低收入和中等收入国家 (LMICs) 中,VPD的患病率和影响在很大程度上是未知的.
  • 由于资源限制,目前对VPD的诊断和治疗机会在LMICs是有限的.

研究的目的:

  • 开发和验证用于使用音频样本检测VPD的机器学习模型.
  • 评估模型在多样化的患者群体中识别VPD的性能.

主要方法:

  • 从各种来源收集了患有和没有VPD的患者的音频样本.
  • 一个机器学习模型被开发并使用Python进行训练.
  • 模型的精度,灵敏度和特异性都使用训练和前数据集进行了评估.

主要成果:

  • 该模型在初始测试中实现了100%的精度.
  • 在训练套件上,灵敏度为92.73%,特异性为98.18%.
  • 在前性数据集上,该模型保持了100%的精度,灵敏度为88.89%,特异性为66%.

结论:

  • 一个基于电话的机器学习选工具用于VPD显示了有希望的准确性.
  • 这项技术有可能在全球范围内显著扩大对VPD诊断和治疗的准入.
  • 解决LMIC中未知的VPD负担对于改善患者的治疗结果和减少心理社会发病率至关重要.