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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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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 29, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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使用机器学习预测助听器的结果.

Pauline Roger1, Thomas Lespargot2, Catherine Boiteux1

  • 1Amplifon France, Paris, France.

Audiology & neuro-otology
|February 3, 2025
PubMed
概括

配戴助听器 (HA) 在安静和噪音的情况下显著改善语音理解. 影响HA成功的关键因素包括技术选择,专业调整和患者的坚持,双耳音量平衡被证明是普遍有益的.

关键词:
大数据就是大数据.双耳声响亮度平衡 双耳声响亮度平衡助听器的好处在于助听器的使用.助听器是一种助听器.机器学习是机器学习.

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科学领域:

  • 听力学 听力学是指听力学.
  • 在医疗保健中的数据科学.
  • 听力科学 听力科学

背景情况:

  • 助听器 (HA) 适配在安静和噪音环境下语音可理解性的有效性需要评估.
  • 确定影响HA结果的因素对于优化患者结果至关重要.
  • 在Amplifon中心对2018-2021年HA配件的回顾性分析将探索预测因素.

研究的目的:

  • 测量助听器 (HA) 配件在安静和噪音环境下对语音理解的有效性.
  • 确定影响HA适配结果的重要因素.
  • 探索和分类HA结果的预测因素,包括技术,专业调整和患者使用.

主要方法:

  • 在2018年至2021年期间安装的77,661名HA用户的回顾性分析.
  • 利用极端梯度增强机器学习来识别HA结果的预测因素.
  • 雇佣的夏普利添加剂解释价值分析以评估个别因素的影响.

主要成果:

  • 在安静和噪音的情况下,HA配件显著提高了语音可理解性.
  • 结果受到HA技术,适配参数 (例如放大,双耳声响度平衡) 和治疗坚持的影响.
  • 双耳声音平衡对所有患者都显示出一致的好处.

结论:

  • 大数据分析对于评估HA结果的预测因素是有效的.
  • 听力护理专业人员在通过技术选择,装配和随访来最大限度地提高患者的治疗结果方面发挥着至关重要的作用.
  • 来自异质群体的结果需要谨慎解释,并且可能受益于基于听力学资料的患者聚类.