相关实验视频

Updated: Jun 14, 2025

Neuro-rehabilitation Approach for Sudden Sensorineural Hearing Loss

Neuro-rehabilitation Approach for Sudden Sensorineural Hearing Loss

Published on: January 25, 2016

预测听力恢复与深度学习算法在突然的神经神经感官听力损失的听力恢复

Hee Won Seo¹, Young Jae Oh², Jaehoon Oh³

¹Department of Otolaryngology-Head and Neck Surgery, College of Medicine, Hanyang University, Seoul, Republic of Korea.

Scientific reports

|August 29, 2024

概括

一个深度学习模型准确地预测了异形突发感神经听力损失 (SSNHL) 患者的听力恢复. 这一进步有助于制定个性化治疗策略,以获得更好的患者结果.

科学领域:

耳鼻喉科耳鼻喉科耳鼻喉科
人工智能的人工智能
医学预测医学预测

背景情况:

异形性突发感神经听力损失 (SSNHL) 是一个重大的临床挑战.
准确的预后对于有效的患者管理和治疗计划至关重要.

研究的目的:

开发和验证一种深度学习模型,用于预测SSNHL患者的听力恢复.
用已确定的听觉学标准来评估模型的准确性.

主要方法:

在2015年1月至2023年5月期间治疗的1108名SSNHL患者的回顾性分析.
一个双层深度学习分类模型,使用多层感知子 (MLP) 和元分类器.
K-fold交叉验证与10个分割用于模型验证.

主要成果:

该模型实现了0.892准确度和0.922 AUC来预测完全恢复 (西格尔标准).
该模型显示0.847准确度和0.918AUC用于A类预测 (AAO-HNS分类).

结论:

深度学习为预测SSNHL听力恢复提供了一个有希望的方法.
开发的模型有可能为个性化患者管理策略提供信息.

关键词:

人工智能的人工智能深度学习是一种深度学习.预测预后预测预测突然的听力损失是什么?

更多相关视频

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Published on: December 9, 2022

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

相关概念视频

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.