Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Topical Therapy for Atopic Dermatitis: What is New and the New Paradigm.

Immunology and allergy clinics of North America·2026
Same author

Normal Hearing Thresholds in Korean Adults Aged 20-79 Years: Establishing Reference Values and Comparison With International Organization for Standardization 7029:2017.

American journal of audiology·2026
Same author

Development of Hearing Information Booklet for Dementia Healthcare Professionals.

Journal of audiology & otology·2026
Same author

Engineering MSC Migration: Roles of Nanoparticles in Activating Migratory Pathways and Functions.

International journal of molecular sciences·2026
Same author

Post-traumatic benign paroxysmal positional vertigo: mechanisms, clinical phenotypes, and a structured clinical pathway for management.

Frontiers in neurology·2026
Same author

Feasibility study on noise attenuation and stability of active noise cancelling headphones for mobile hearing screening.

Hearing research·2026

相关实验视频

Updated: Sep 19, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.6K

通过深度学习对音频录像进行数字化:用于听取大数据的结构化数据提取和假名化.

Sunghwa You1, Chanbeom Kwak2, Chul Young Yoon1

  • 1Research Institute of Hearing Enhancement, Yonsei University Wonju College of Medicine, Wonju, South Korea; Department of Medical Informatics and Biostatistics, Yonsei University Wonju College of Medicine, Wonju, South Korea.

Hearing research
|June 18, 2025
PubMed
概括

这项研究引入了一个深度学习系统,以数字化纯色音频计量 (PTA) 图像,将其转换为结构化数据,用于听力大数据计划. 人工智能模型显著提高了效率和准确性,使其能够更好地集成到电子病历中.

关键词:
音频节目 音频节目 音频节目深度学习算法深度学习算法数字化 数字化听取大数据的听力伪名化 伪名化

更多相关视频

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K
Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique
11:39

Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique

Published on: September 7, 2022

2.3K

相关实验视频

Last Updated: Sep 19, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.6K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K
Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique
11:39

Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique

Published on: September 7, 2022

2.3K

科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 听力学 听力学是指听力学.

背景情况:

  • 纯色调听力测量 (PTA) 对于诊断听力损失至关重要,但听力图像通常是无结构的.
  • 这种缺乏结构阻碍了对电子病历 (EMR) 和常用数据模型 (CDM) 的整合,限制了大规模数据分析.
  • 开发自动化数字化方法对于推进听力大数据研究至关重要.

研究的目的:

  • 开发和验证基于深度学习的系统,用于自动数字化音频记录.
  • 将非结构化的音频图像转换为结构化的数字数据,适用于EMR和CDM.
  • 提高临床和研究应用的听力数据的可访问性和可扩展性.

主要方法:

  • 训练了一个卷积神经网络 (CNN) 来从音频图中提取频率和值.
  • 该系统包含预处理,图案分类,图像分析和后处理的模块.
  • 使用光学字符识别 (OCR) 来提取患者数据,随后进行伪名化以确保数据隐私.

主要成果:

  • 深度学习模型实现了高精度:95.01%的右耳和98.18%的左耳.
  • 与手工方法相比,数字化速度增加了17.72倍,使每个音频录像的处理时间从63.27秒减少到3.57秒.
  • 由此产生的结构化数据格式可在遵守隐私法规的情况下,便于无集成到大数据平台和CDM中.

结论:

  • 开发的系统显著提高了音频记录数字化的效率和准确性.
  • 这便于创建全面的听力大数据,支持人工智能驱动的诊断,并使大规模的听力数据分析成为可能.
  • 该框架确保了结构化的数值数据输出,克服了以往以分类为中心的研究的局限性,并符合数据伪名化要求.