多级呼吸声分析:信心驱动的喘息和裂纹检测
IEEE transactions on bio-medical engineering
|September 23, 2025
概括
这项研究为自动化儿科听觉提供了一个以信任为导向的框架,改进了对喘息和裂等呼吸系统疾病的检测. 多阶段方法通过优先考虑高确定性声音预测来提高诊断准确性.
科学领域:
- 医疗信息学 医疗信息学
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
背景情况:
- 准确检测偶然的呼吸声 (喘息,) 对于诊断儿科呼吸系统疾病至关重要.
- 自动化听力分析为改善诊断准确性和临床工作流程提供了潜力.
研究的目的:
- 引入一个多阶段的,信任驱动的框架,用于自动化儿科听力分析.
- 进行三向分类 (正常,喘息,) 以提高诊断准确度.
主要方法:
- 开发一个管道,整合异常特定的细分选择,细分级别分类和基于信任的融合.
- 利用对比的变异性循环神经网络 (CVRNN) 进行增强的特征提取.
- 通过7个国家的742名受试者的多样化儿科数据集进行验证.
主要成果:
- 对异常特异的细分选择实现了98.47%的回忆,用于识别偶然的呼吸区域.
- 分段级分类器显示出72.15%的喘息精度和68.1%的裂精度.
- 这种以信任为导向的融合最终产生了62.12%的三向分类准确度,超过了传统的聚合方法.
结论:
- 基于信心的多阶段方法通过优先考虑高确定性预测来增强自动呼吸声分类.
- 该框架推进了计算机辅助呼吸道诊断,用于早期检测和监测儿科呼吸道疾病.
- 改善临床工作流程和查的潜力,特别是在资源有限的环境中.
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