查和诊断中的咳声音:一个范围广泛的审查
Siddhi Hegde1, Shreya Sreeram1, Isaac L Alter2
1KVG Medical College and Hospital, Sullia, India.
The Laryngoscope
|September 6, 2023
概括
咳声音显示出可访问的,非侵入性疾病查工具的希望,特别是机器学习和移动技术的进步. 然而,目前的研究往往使用非标准化的方法和有限的数据集,影响可靠性.
科学领域:
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
- 声学信号处理 声学信号处理
背景情况:
- 用于诊断的咳声音分析有着悠久的历史,但最近学术兴趣加速.
- 咳声音作为一种低成本,非侵入性疾病生物标志物的潜力是显著的.
- 无处不在的移动技术为咳分析提供了高质量的记录功能.
研究的目的:
- 审查生物医学和工程文献中咳声音的应用,用于查和诊断.
- 专注于疾病类型,数据收集,处理,分析,准确性和限制.
- 综合当前的研究趋势,并确定研究缺口.
主要方法:
- 对多个数据库 (PubMed,EMBASE,Scopus等) 的综合范围审查. 进行了.执行了.
- 从创立到2021年8月,搜索文献,包括同行评审和灰色文献.
- 包括对人类和动物咳声音的查和诊断用途的研究.
主要成果:
- 108篇文章符合被选的438篇摘要中的入选标准.
- 人类研究占主导地位 (77.8%),主要关注成年人 (57.3%).
- 机器学习被用于61.1%的研究,其中大多数是在2010年后发表的. 常见的重点包括咳检测 (41.7%) 和COVID-19查 (11.1%). 在儿科研究中,喘诊断很普遍 (52.6%).
结论:
- 咳声音分析是一个快速增长的领域,由机器学习和移动技术驱动.
- 非标准化的数据收集协议和有限的数据集有效性仍然是重大挑战.
- 需要进一步研究标准化的多式联运数据收集,以提高诊断准确性和外部有效性.
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