深度学习的应用用于使用呼吸道声音记录进行支气管喘诊断
Theodore Aptekarev1, Vladimir Sokolovsky1, Evgeny Furman2
1Physics Department, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
PeerJ. Computer science
|June 22, 2023
概括
深度学习分析呼吸声以诊断喘. 这种计算机辅助方法可以准确地识别所有年龄段和疾病阶段的患者的喘,有助于早期检测和监测.
科学领域:
- 医疗信息学 医疗信息学
- 肺部病理学 肺部病理学
- 人工智能的人工智能
背景情况:
- 支气管喘诊断可能具有挑战性,特别是在早期阶段.
- 对呼吸声的客观评估对于准确诊断至关重要.
- 当前的诊断方法在可访问性和早期检测方面可能存在局限性.
研究的目的:
- 开发和验证一种基于深度学习的计算机辅助诊断工具,用于支气管喘.
- 评估开发的软件在分类呼吸声中用于喘检测的准确性.
- 评估该方法用于临床前查和患者监测的潜力.
主要方法:
- 一个匿名数据库累积了1371个呼吸声记录 (1238名患者,133名志愿者).
- 使用深度学习技术来训练软件在各种身体部位的呼吸声记录.
- 实施"生病/健康"和"喘/非喘"诊断的二元分类.
主要成果:
- "生病/健康"分类器在小型试验样本上实现了88%的灵敏度,86%的特异性和87%的准确性.
- "喘/非喘"分类器显示了92%的灵敏度,82%的特异性和87%的准确性.
- 该模型在941名喘患者记录中的93%正确诊断了喘.
结论:
- 对呼吸道声音的深度学习分析提供了一种高度准确的计算机辅助方法来诊断支气管喘.
- 开发的方法在不同患者的人口统计和疾病特征方面是有效的.
- 这种方法可以作为一种有价值的查工具,并支持远程患者监测和治疗评估.
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