测试一种反向建模方法,使用梯度增强回归来估计中风体积,使用患者热稀释数据
Vasiliki Vicky Bikia1, Dionysios Adamopoulos2,3,4, Marco Roffi2,4
1Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland.
Frontiers in artificial intelligence
|April 2, 2025
概括
一种新的机器学习方法使用1D循环模型准确地估计了中风体积 (SV). 这种非侵入性方法为重症监护和手术患者提供了更快,更精确的心血管评估.
科学领域:
- 心血管生理学心血管生理学
- 生物医学工程 生物医学工程
- 机器学习在医学中的应用
背景情况:
- 冲击体积 (SV) 对于评估心血管功能,心脏表现和血液流量充足度至关重要.
- 精确的SV测量对于管理心力衰竭,手术患者和重症监护至关重要.
- 传统的侵入性方法 (如热稀释) 和不精确的非侵入性技术限制了持续的监测.
研究的目的:
- 开发一种新的,非侵入性的方法来准确估计中风体积.
- 整合一个经过验证的1D系统循环模型与机器学习用于SV预测.
- 以更快,基于回归的方法取代传统的优化流程.
主要方法:
- 开发了一个梯度增强回归模型用于SV估计.
- 利用一个模拟1D系统循环动态的in silico生成的数据集.
- 在24名患者中与黄金标准热稀释技术对比,验证了该方法.
主要成果:
- 基于ML的方法显示了与热稀释的满意一致 (MAE: 16 mL,nRMSE: 21%,Bias: -9.2 mL,LoA: [-47, 28] mL).
- 发现了显著的相关性 (r=0.7,p<0.05).
- 预测的SV (68±23毫升) 与参考的SV (77±26毫升) 相比略微低估.
结论:
- 新的ML增强的1D模型提供了准确和快速的SV估计.
- 减少的计算时间使该方法适合实时临床应用.
- 这种非侵入性方法为持续的血液动力学监测提供了一个有希望的替代方案.
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