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Hearing

Hearing

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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Updated: May 30, 2025

Functional Imaging of Auditory Cortex in Adult Cats using High-field fMRI

Functional Imaging of Auditory Cortex in Adult Cats using High-field fMRI

Published on: February 19, 2014

基于功能性和结构性MRI的噪音引起的听力损失的研究,使用机器学习方法.

Minghui Lv¹, Liping Wang¹, Ranran Huang¹

¹Imaging Department, Yantaishan Hospital, Yantai, China.

Scientific reports

|January 26, 2025

概括

机器学习模型现在可以使用脑成像来分类噪声引起的听力损失 (NIHL). 功能性MRI (fMRI) 数据与机器学习相结合,在识别个人NIHL时显示出高准确性.

关键词:

功能性磁共振成像技术功能性磁共振成像技术机器学习是机器学习.噪音引起的听力损失结构磁共振成像技术结构磁共振成像技术

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Modified Experimental Conditions for Noise-Induced Hearing Loss in Mice and Assessment of Hearing Function and Outer Hair Cell Damage

Modified Experimental Conditions for Noise-Induced Hearing Loss in Mice and Assessment of Hearing Function and Outer Hair Cell Damage

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Functional Neuroimaging Using Ultrasonic Blood-brain Barrier Disruption and Manganese-enhanced MRI

Functional Neuroimaging Using Ultrasonic Blood-brain Barrier Disruption and Manganese-enhanced MRI

Published on: July 12, 2012

相关实验视频

Last Updated: May 30, 2025

Functional Imaging of Auditory Cortex in Adult Cats using High-field fMRI

Functional Imaging of Auditory Cortex in Adult Cats using High-field fMRI

Published on: February 19, 2014

Modified Experimental Conditions for Noise-Induced Hearing Loss in Mice and Assessment of Hearing Function and Outer Hair Cell Damage

Modified Experimental Conditions for Noise-Induced Hearing Loss in Mice and Assessment of Hearing Function and Outer Hair Cell Damage

Published on: February 10, 2023

Functional Neuroimaging Using Ultrasonic Blood-brain Barrier Disruption and Manganese-enhanced MRI

Functional Neuroimaging Using Ultrasonic Blood-brain Barrier Disruption and Manganese-enhanced MRI

Published on: July 12, 2012

科学领域:

神经成像是一种神经成像.
机器学习机器学习
职业健康职业健康职业健康

背景情况:

噪音引起的听力损失 (NIHL) 是一个普遍的职业健康问题.
当前的诊断方法可能无法完全捕捉与NIHL相关的潜在神经变化.
先进的神经成像和机器学习为改进NIHL分类提供了潜力.

研究的目的:

为NIHL开发和评估基于机器学习的分类模型.
整合功能磁共振成像 (fMRI) 和结构磁共振成像 (sMRI) 数据.
确定最佳的神经成像特征,以区分NIHL患者和健康个体.

主要方法:

提取的fMRI指数 (ALFF,fALFF,ReHo,DC) 和sMRI指数 (GMV,WMV,皮层厚度).
使用最小绝对收缩和选择操作员 (LASSO) 进行特征选择.
使用支持矢量机 (SVM),随机森林 (RF) 和后勤回归 (LR) 进行模型开发.

主要成果:

集成fMRI指数的SVM模型实现了最高的性能.
实现了 0.97.9 的接收器运行特征曲线 (AUC) 下的面积.
在识别NIHL时证明了95%的分类准确性.

结论:

集成fMRI指标的SVM分类模型显示了NIHL识别的重大潜力.
fMRI指标在NIHL的分类中发挥着补充作用.
结合多个脑成像指标对于强大的NIHL分类模型至关重要.