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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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.</div></div><div><h3>Results:</h3><div>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.</div></div><div><h3>Conclusion:</h3><div>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.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"88 ","pages":"Article 102606"},"PeriodicalIF":3.7000,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A virtual population cohort approach for fetal cardiac valve modeling\",\"authors\":\"Bettine G. van Willigen , Nick van Osta , M. Beatrijs van der Hout-van der Jagt , Frans N. van de Vosse , Wouter Huberts\",\"doi\":\"10.1016/j.jocs.2025.102606\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Introduction:</h3><div>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.</div></div><div><h3>Method:</h3><div>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.</div></div><div><h3>Results:</h3><div>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.</div></div><div><h3>Conclusion:</h3><div>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.</div></div>\",\"PeriodicalId\":48907,\"journal\":{\"name\":\"Journal of Computational Science\",\"volume\":\"88 \",\"pages\":\"Article 102606\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2025-05-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computational Science\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1877750325000833\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Science","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1877750325000833","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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.
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).