机器学习驱动的肺声分析:喘诊断的新方法
Ihsan Topaloglu1, Gulfem Ozduygu1, Cagri Atasoy1
1Department of Pulmonology, Faculty of Medicine, Kafkas University, 36000 Kars, Turkey.
Advances in respiratory medicine
|September 22, 2025
概括
这项研究开发了一种机器学习算法,使用肺声分析来准确诊断喘,即使是在正常螺旋测量患者中也是如此. 非侵入性方法为早期喘检测提供了一种有效的工具.
科学领域:
- 呼吸系统医学 呼吸系统医学
- 医疗保健中的人工智能
- 诊断技术 诊断技术的使用
背景情况:
- 喘是一种慢性呼吸道疾病,症状可变,在得到良好控制的情况下通常呈现正常,使诊断复杂化.
- 传统的诊断方法,如螺旋测量和支气管挑测试有局限性,包括侵入性和诱导支气管收缩的潜力.
研究的目的:
- 开发一种非侵入性,客观和可重复的诊断方法,用于早期检测喘.
- 利用基于机器学习的肺声分析来识别喘,即使在正常螺旋计时的稳定时期.
主要方法:
- 开发了一种机器学习算法来分类受控喘患者和健康人群,使用呼吸声的数字耳机录音.
- 招募了120名参与者 (60名喘患者,60名健康患者). 呼吸道声段使用Mel-Frequency Cepstral Coefficients (MFCCs) 和Tunable Q-Factor Wavelet Transform (TQWT) 进行了分析.
- 使用ReliefF选择的特征用于训练正方形支向量机 (SVM) 和狭窄神经网络 (NNN) 模型.
主要成果:
- 肺功能测试显示,喘组FEV1和FEV1/FVC比率较低,尽管在正常范围内.
- 四边形SVM模型达到99.86%的准确性,正确分类了99.44%的对照和99.89%的喘病例.
- 狭窄神经网络模型实现了99.63%的准确性,灵敏度,特异性和F1分数超过99%.
结论:
- 开发的机器学习算法能够准确诊断喘,即使在有正常螺旋测量和临床发现的患者中也是如此.
- 这种方法为早期喘检测提供了一个非侵入性,客观和高效的诊断工具.
- 肺部声音分析与机器学习相结合,显示出改善喘诊断的巨大潜力.
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