基于深度学习的肺声分析用于智能耳语镜
Dong-Min Huang1, Jia Huang2, Kun Qiao2
1Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.
Military Medical Research
|September 25, 2023
概括
深度学习通过自动分析肺部声音来显著推进呼吸系统疾病的诊断. 这篇评论探讨了人工智能算法,数据集和智能听力镜的方法,解决当前的挑战并提供一个开源框架.
科学领域:
- 医疗技术 医学技术
- 人工智能的人工智能是人工智能.
- 呼吸系统药物 呼吸系统药物
背景情况:
- 传统的听觉有诸如主观性和无法录制声音等局限性.
- 数字耳机可存储和共享声音,促进远程医疗和教育.
- 机器学习,特别是深度学习,为智能诊断提供自动化肺声分析.
研究的目的:
- 为肺部声音分析提供深度学习算法的全面概述.
- 突出人工智能 (AI) 在推进呼吸道诊断中的作用.
- 介绍一个开源框架,用于在这个领域标准化深度学习工作流程.
主要方法:
- 对应用到肺部声音分析的深度学习算法的审查.
- 专注于将肺部声音转换为2D谱图,用于卷积神经网络 (CNN) 分析.
- 检查任务类别,公共数据集,除技术和最先进的方法.
主要成果:
- 深度学习能够端到端识别呼吸系统疾病和异常的肺部声音.
- 确定的挑战包括设备的可变性,噪声灵敏度和模型的可解释性.
- 提供了一个开源框架,以提高可重现性和标准化.
结论:
- 深度学习对智能耳机发展和自动呼吸道诊断具有重大潜力.
- 应对当前的挑战对于广泛的临床采用至关重要.
- 拟议的框架旨在促进合作,并推进人工智能驱动的肺声分析的研究.
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