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相关概念视频

Hearing01:31

Hearing

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When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
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Correction: Kim et al. The Suppression of Ubiquitin C-Terminal Hydrolase L1 Promotes the Transdifferentiation of Auditory Supporting Cells into Hair Cells by Regulating the mTOR Pathway. <i>Cells</i> 2024, <i>13</i>, 737.

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相关实验视频

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Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
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听觉脑干响应数据预处理方法用于听力损失患者的自动分类.

Jun Ma1, Jae-Hyun Seo2, Il Joon Moon3

  • 1Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea.

Diagnostics (Basel, Switzerland)
|December 9, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了用于听觉脑干响应 (ABR) 图形数据的预处理方法,以提高深度学习在检测听力损失中的准确性. 标准化数据提高了人工智能模型的性能,用于客观的听力评估.

关键词:
这就是ABR ABR.这是VGGGG.深度学习是一种深度学习.听力损失 听力损失是什么图像处理是图像处理的过程.

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相关实验视频

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科学领域:

  • 生物医学工程 生物医学工程
  • 神经科学是一个神经科学.
  • 医学成像分析 医学成像分析

背景情况:

  • 听觉脑干反应 (ABR) 测试通过通过听觉神经测量脑干电信号来客观地评估听力.
  • 对于无法提供主观反的人来说,ABR至关重要,例如婴儿,老年人和残疾患者.
  • 当前的ABR数据在设备上的变化给一致的分析和AI模型培训带来了挑战.

研究的目的:

  • 为听觉脑干响应 (ABR) 图形数据提出一个图像预处理管道.
  • 为了标准化各种ABR图像数据,以提高深度学习模型的性能.
  • 评估深度学习模型在使用预处理的ABR数据对听力损失进行分类时的有效性.

主要方法:

  • 开发了一种图像预处理技术,以标准化来自各种测量设备的ABR图形数据.
  • 在分类任务中应用了VGG16卷积神经网络 (CNN) 模型.
  • 在10,000张预处理的ABR图像数据集上训练和测试VGG16模型,以分类听力损失的存在或缺失.

主要成果:

  • 标准化的ABR图像数据提高了深度学习模型的性能.
  • VGG16模型在使用预处理数据对听力损失的存在或不存在进行分类时表现出了准确性.
  • 对各种权重的分析验证了模型的分类学习能力.

结论:

  • 拟议的图像预处理方法有效地创建用于深度学习的标准化ABR数据集.
  • 这种方法可以提高人工智能驱动的听力损失检测的准确性.
  • 这些发现为预处理和学习医疗图形数据 (包括ABR图形) 设定标准提供了基础.