自动轻量级模型用于使用呼吸道和咳声音信号检测喘
Shuting Xu1,2, Ravinesh C Deo1, Oliver Faust3
1Artificial Intelligence Applications Laboratory, School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia.
Diagnostics (Basel, Switzerland)
|May 14, 2025
概括
这项研究引入了一个使用咳和呼吸道声音来诊断喘和COPD的AI系统. 双声信号方法实现了高精度,改善了早期检测和临床管理.
科学领域:
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
- 呼吸系统医学 呼吸系统医学
背景情况:
- 喘和COPD等慢性呼吸道疾病对全球健康构成重大挑战.
- 准确和及时的诊断对于有效的管理和治疗至关重要.
- 当前的诊断方法在某些环境中可能具有侵入性或难以使用.
研究的目的:
- 开发和验证基于人工智能的系统,用于使用咳和呼吸声对喘,COPD和健康个体进行分类.
- 为了提高诊断准确度,利用双声信号.
- 创建轻量级,自动化的深度学习模型,以改善早期检测和临床管理.
主要方法:
- 使用机器学习算法 (RF,SVM,DT,NN,KNN) 集成到AI模型中.
- 员工5倍交叉验证,以进行强大的模型培训和验证.
- 应用了Gabor时间频率转换用于特征提取和NCA用于特征选择.
- 实施了多数投票组合技术,以提高诊断可靠性.
- 进行了单独的实验,使用呼吸和咳声音子集进行有针对性的评估.
主要成果:
- 组合模型使用呼吸声实现了94.05%的准确性,使用咳声音实现了83.31%的准确性,用于喘与正常差异化.
- 从整合多个分类器模型和声音模式中获得显著的好处.
- 为未来的呼吸道疾病诊断展示了前所未有的准确性和稳定性.
结论:
- 整合咳和呼吸道声音的人工智能系统为呼吸系统疾病检测设定了新的基准.
- 双声分析方法在早期发现和治疗喘和COPD方面取得了进展.
- 拟议的模型显示了改变喘诊断实践和支持临床决策的强大潜力.
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