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Related Experiment Video

Updated: Jul 4, 2026

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

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Published on: December 6, 2024

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

F Argus¹, S A Creamer¹, R Nicholson²

¹Auckland Bioengineering Institute, University of Auckland, New Zealand.

Computer Methods and Programs in Biomedicine

|July 2, 2026

Summary

This summary is machine-generated.

Accounting for surrogate model errors improves cardiac model parameter estimation and uncertainty quantification. This approach enhances inference accuracy and reduces computational costs for clinical applications like cardiac stiffness biomarker assessment.

Keywords:

Bayesian approximation error Cardiac mechanics Surrogate modelling Uncertainty quantification

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Last Updated: Jul 4, 2026

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Area of Science:

Computational modeling
Biophysics
Medical imaging and diagnostics

Background:

Parameter estimation for complex cardiac models is computationally intensive.
Surrogate models accelerate evaluations but introduce errors.
Neglecting these errors can lead to biased or overconfident inferences.

Purpose of the Study:

To present a general framework for accounting for model errors in surrogate-based parameter estimation and uncertainty quantification.
To improve the accuracy and reliability of inferences from cardiac models.

Main Methods:

Utilized the Bayesian approximation error approach for a general framework.
Implemented the framework for cardiac stiffness estimation using 3D left ventricle passive deformation data.
Compared a neural network surrogate with a simple regression approach.

Main Results:

Neglecting model errors, even with sophisticated neural networks, resulted in biased and overconfident estimates.
The proposed framework enabled model-error corrections, yielding substantially improved inferences.
The approach reduced the number of simulations and computational cost by augmenting neural networks with Bayesian approximation error models.

Conclusions:

A novel framework was developed to augment surrogate models, enhancing inference and reducing training time.
This method holds potential for efficient clinical estimation of cardiac stiffness as a disease biomarker.