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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
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通过减少顺序建模和分类来导出表型代表的左心室流动模式.

María Guadalupe Borja1, Pablo Martinez-Legazpi2, Cathleen Nguyen3

  • 1Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, USA.

Computers in biology and medicine
|June 30, 2024
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概括
此摘要是机器生成的。

减少顺序模型 (ROM) 将复杂的心脏流动模式简化为可解释的指标. 这种机器学习方法有效地区分了扩张性心肌病 (DCM),高性心肌病 (HCM) 和使用心声图数据的健康个体.

关键词:
血液流动成像 血液流动成像心脏衰竭是因为心脏衰竭.机器学习是机器学习.主要组件分析的主要组件分析.矢量流量映射映射 矢量流量映射

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科学领域:

  • 心血管成像 - 心血管成像
  • 生物医学工程 生物医学工程
  • 计算流体动力学的流体动力学.

背景情况:

  • 先进的心脏流量成像的临床翻译受到提取代表性流量模式和指标的挑战的阻碍.
  • 减少顺序模型 (ROM) 提供了一个有前途的策略,用于导出简单的,可解释的心室内流量指标.
  • 将ROM与机器学习 (ML) 集成,可以提高心脏病患者的诊断和风险分层.

研究的目的:

  • 为了研究从二维彩色多普勒回声心电图中获得的ROM的实用性,用于分类心脏病状况.
  • 开发和验证一种简单的,可解释的指标,以区分非缺血性扩张性心肌病 (DCM),高性心肌病 (HCM) 和健康对照.
  • 探索基于ML的ROM分析在心脏流量评估中的临床应用的潜力.

主要方法:

  • 适当的直角分解 (POD) 应用于81名DCM患者,51名HCM患者和77名对照者的2D彩色多普勒回声心电图,以构建患者和队列特定的ROM.
  • 在ROM上测试了三个ML分类器,使用超参数优化来最大限度地提高监督模型中的分类能力.
  • 用矢量流量映射来可视化和解释流量模式和ML结果.

主要成果:

  • 基于POD的ROM有效地代表了所有队列,主要模式捕获了80%以上的流动动能.
  • 第二个 () 和第一个 (喷气) POD 模式之间的动能比,称为对喷气 (V2J) 能量比,成为一个关键的区分度量.
  • V2J比率在区分DCM,HCM和对照组方面取得了很高的准确性,ROC曲线下的面积在0.81到0.95.5之间.

结论:

  • 使用POD的模态分解可以生成ROM,捕获基本的心脏流动动力学.
  • 简单的,可解释的流量指标,如V2J能量比率,可以从这些ROM中导出.
  • 这些指标显示出区分心脏病状态的巨大潜力,非常适合基于ML的分析.