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相关概念视频

The Cochlea01:13

The Cochlea

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The cochlea is a coiled structure in the inner ear that contains hair cells—the sensory receptors of the auditory system. Sound waves are transmitted to the cochlea by small bones attached to the eardrum called the ossicles, which vibrate the oval window that leads to the inner ear. This causes fluid in the chambers of the cochlea to move, vibrating the basilar membrane.
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相关实验视频

Updated: May 12, 2025

Author Spotlight: Advancements in Impedance Monitoring for Cochlear Implant Surgery
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基于神经响应遥测值的耳植入物动态范围参数估计方法.

Yao Liu1,2,3,4,5, Yue Wang1,2,3,4,5, Yu Chen1,2,3,4,5

  • 1Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China.

Acta oto-laryngologica
|May 10, 2025
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概括

神经响应遥测 (NRT) 值与耳植入体 (CI) 患者的行为测试值相关. 深度学习模型准确地预测CI调整参数,有助于术后调整,特别是对于内耳形病例.

关键词:
耳植入器是一种耳植入器.深度学习是一种深度学习.神经响应远程测量神经响应.主观行为测试 测试主观行为测试

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科学领域:

  • 听力学 听力学是指听力学.
  • 生物医学工程 生物医学工程
  • 机器学习 机器学习

背景情况:

  • 在耳植入物 (CI) 患者中,主观行为测试值和神经响应遥测 (NRT) 值之间的相关性研究缺乏.
  • 对CI调整参数缺乏可靠,客观的预测模型.

研究的目的:

  • 调查主观行为测试值和NRT值在CI患者与正常的耳形态和内耳形 (IEM) 之间的相关性.
  • 评估深度学习对预测CI机器调整参数和指导术后调整的有用性.

主要方法:

  • 使用特定电极对57名正常耳 (NC) 和20名IEMCI患者进行了NRT和主观行为测试.
  • 分析了NRT值和T/C值之间的相关性.
  • 开发了一个基于卷积神经网络的深度学习模型来预测CI机器调整参数.

主要成果:

  • 在NC和IEM组中,NRT值与T和C值有显著的相关性.
  • IEM组的平均T值,C值和NRT值略高于NC组.
  • 开发的深度学习模型在预测实际CI调整参数方面表现出很高的准确性.

结论:

  • NRT值与主观行为值有显著的关系,有助于CI调整.
  • 与NC患者相比,IEM患者建议采用不同的机器调整策略.
  • 神经网络预测模型可以有效指导术后CI调整.