自动化系统用于诊断肺纤维化使用破裂分析记录的肺声音,基于代的信封平均分形维度过器的肺纤维化
Ravi Pal1, Anna Barney1, Giacomo Sgalla2
1Institute of Sound and Vibration Research (ISVR), University of Southampton, Southampton, United Kingdom.
Physiological measurement
|January 17, 2025
概括
一个自动化系统通过分析肺部声音中的裂纹来准确检测肺纤维化 (PF). 这种早期检测工具有助于全科医生,有可能改善这种严重的肺部疾病的患者结果和生存率.
科学领域:
- 肺部医学 肺部医学
- 医学诊断 医学诊断 医学诊断
- 信号处理 信号处理
背景情况:
- 肺纤维化 (PF) 的延迟诊断与死亡率的增加有关.
- 早期发现和治疗PF对于改善预期寿命和生活质量至关重要.
- 肺部声音的裂纹是早期PF诊断的潜在指标.
研究的目的:
- 开发和评估一种自动化系统,以区分与PF相关的肺声音与其他病理状况.
- 为了利用每次呼吸周期 (NOC/BC) 的平均裂纹数量进行自动分类.
- 根据专家医生评估和放射学发现,评估系统的性能.
主要方法:
- 设计了一个自动化系统,涉及预处理,使用代信封平均碎形维度过器进行裂纹分离,裂纹验证/计数以及NOC/BC估计.
- 该系统在48名受试者身上进行了测试 (24名有PF,24名没有).
- 对两个专家呼吸系统医生的诊断和放射科医生审查的高分辨率计算机断层扫描 (HRCT) 图像进行了评估.
主要成果:
- 自动分类器的AUC值为0.845 (95%CI为0.739-0.952,p<0.001),具有91.7%的灵敏度和59.3%的特异性,NOC/BC切线为18.65.
- 与医生的平均表现相比,该系统的表现有利 (83.3%的灵敏度,56.25%的特异性).
- 该系统显示出强大的诊断支持潜力,用于识别疑似间歇性肺部疾病 (ILD) 的患者.
结论:
- 自动化肺声分析系统显示出作为肺纤维化诊断支持工具的前景.
- 它可以帮助全科医生识别需要对ILD进行进一步调查的患者.
- 虽然放射性评估仍然是黄金标准,但该系统为早期辅助性评估提供了有价值的辅助.
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