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一个开源的基于深度学习的GUI工具箱用于自动听觉脑干响应分析 (ABRA).

Abhijeeth Erra1, Jeffrey Chen1, Cayla M Miller2

  • 1Data Institute, University of San Francisco, San Francisco, CA.

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科学领域:

  • 神经科学是一个神经科学.
  • 生物工程是生物工程.
  • 计算生物学 计算生物学

背景情况:

  • 听力损失是一个全球性健康问题,与认知能力下降有关.
  • 听觉脑干反应 (ABR) 对于评估听觉通路功能至关重要.
  • 目前的ABR分析是主观的,限制了可复制性.

研究的目的:

  • 开发一种用于分析ABR波形的自动化工具.
  • 为了提高研究的一致性,将ABR计量提取标准化.
  • 提高听力损失诊断的效率和可靠性.

主要方法:

  • 开发了听觉脑干响应分析器 (ABRA),这是一个开源的,深度学习驱动的GUI.
  • 利用在各种ABR数据集上训练的卷积神经网络.
  • 自动提取ABR指标,如峰值振幅,延迟和听觉值.

主要成果:

  • 阿布拉实现了与专家人类分析相美的性能.
  • 该工具显著减少了ABR分析时间.
  • 在多实验室数据集中证明了增强的可重现性.

结论:

  • 阿布拉提供自动化,标准化的ABR分析,克服了传统的局限性.
  • 该工具促进了在听力研究和神经科学中的跨学科见解.
  • 在线可以访问ABRA,促进更广泛的采用和协作.