胎儿心脏瓣膜建模的虚拟群体队列方法

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Bettine G. van Willigen , Nick van Osta , M. Beatrijs van der Hout-van der Jagt , Frans N. van de Vosse , Wouter Huberts
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引用次数: 0

摘要

胎儿心血管生理学的数学模型为研究胎儿循环系统提供了有价值的见解。在0D和1D模型中,胎儿心脏瓣膜通常表示为二极管,提供简单和可扩展性,但无法捕捉真实的瓣膜行为,并可能导致不切实际的压降。基于伯努利方程的更精确的模型更真实地捕获了瓣膜的动态行为,但它们需要针对特定情况不断调整,这对胎儿心脏生长的模拟具有挑战性。方法:本研究引入了一种基于贝叶斯推理的虚拟人口队列方法,作为解决这一挑战的方法。通过将该方法应用于40周龄胎儿的标准化主动脉瓣模型,证明了其在识别反映健康胎儿主动脉瓣行为的输入参数分布方面的有效性。结果:该方法包括定义模板模型和确定适当的参数空间来模拟生理行为。贝叶斯推理方法有助于这些参数的识别,从而形成一个虚拟的人群队列,该队列紧密地代表了真实的生理相关胎儿主动脉瓣状况。结论:研究结果表明,该方法成功地识别了胎儿主动脉瓣模型的虚拟群体队列,包括模型参数的不确定性及其与模型结果的相关性。这种方法提供了一个广泛适用的框架,具有潜在的模型,可以适应胎儿生长的不断变化的生理条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A virtual population cohort approach for fetal cardiac valve modeling

Introduction:

Mathematical models of fetal cardiovascular physiology provide valuable insights when studying the fetal circulatory system. In 0D and 1D models, fetal cardiac valves are often represented as diodes, offering simplicity and scalability but failing to capture realistic valvular behavior and can result in unrealistic pressure drops. More accurate models based on the Bernoulli equation capture valvular dynamic behavior more realistically, but they require constant tuning for specific cases, challenging simulation of fetal cardiac growth.

Method:

This study introduces a virtual population cohort approach informed by Bayesian inference as a solution to this challenge. By applying this method to a standardized aortic valve model of a 40-week-old fetus, it demonstrates its effectiveness in identifying input parameter distributions that reflect healthy fetal aortic valve behavior.

Results:

The approach involves defining a template model and determining an appropriate parameter space to simulate physiological behavior. Bayesian inference method facilitates identification of these parameters, resulting in a virtual population cohort that closely represents real physiological relevant fetal aortic valve conditions.

Conclusion:

The findings show that this approach successfully identifies a virtual population cohort of the fetal aortic valve model, including uncertainty of model parameters and their correlations with model outcomes. This approach offers a widely applicable framework with potential for models that can adapt to the evolving physiological conditions of fetal growth.
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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