简化心力衰竭患者护理与机器学习的胸腔声音数据
Rony Marethianto Santoso1, Wilbert Huang2, Ser Wee3
1Faculty of Medicine, University Indonesia, Jakarta 40416, Indonesia.
World journal of cardiology
|September 30, 2025
概括
机器学习分析胸腔声音以检测心力衰竭 (HF). 这种方法显示出高精度,有望更快地进行临床整合,以改善HF患者的护理.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 胸腔的声音,包括心脏和肺部的声音,提供了重要的诊断信息,特别是在心力衰竭 (HF).
- 人类的听觉能力有限,无法从这些声音中提取全面的数据.
- 人工智能 (AI) 和机器学习 (ML) 为胸部声音数据提供了先进的分析能力.
研究的目的:
- 审查使用机器学习分析心力衰竭患者胸腔声音的研究.
- 总结各种ML模型,特征提取技术和现有研究中使用的分类方法.
- 评估ML在区分正常患者和HF患者以及风险分层方面的潜力.
主要方法:
- 数据预处理:对声音数据进行无声化,重新采样,细分和规范化.
- 特性提取和选择以识别相关的声学标记.
- 应用各种机器学习分类器进行分析和预测.
- 对模型性能进行分类,风险分层和预后评估的评估.
主要成果:
- 研究主要集中在心脏声音分析上,以区分正常和HF患者.
- 一些研究利用整个胸部声音进行HF分类.
- ML模型在各种高频相关分析中显示出有希望的高准确性,包括风险分层.
结论:
- 对胸腔声音的机器学习分析是心力衰竭管理的高度准确的方法.
- 该审查强调了ML模型将被整合到HF患者护理的临床实践中的潜力.
- 建议进行进一步的前性研究,以加快在日常临床工作流程中采用这些人工智能驱动工具.
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