ArticleJournal of the Royal Society, Interface2019
Gaussian process emulation to accelerate parameter estimation in a mechanical model of the left ventricle: a critical step towards clinical end-user relevance.
Article in Journal of the Royal Society, Interface, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Weaving the Digital Tapestry: Methods for Emulating Cohorts of Cardiac Digital Twins Using Gaussian Processes.Annals of biomedical engineering · 2026Article
- Sensitivity analysis of factors in a microfluidics CFD model of coagulation and cardiac applications.Biomechanics and modeling in mechanobiology · 2026Article
- Deep Learning-Based Estimation of Myocardial Material Parameters from Cardiac MRI.Bioengineering (Basel, Switzerland) · 2025Article
- Bridging the gap between mechanistic biological models and machine learning surrogates.PLoS computational biology · 2023Review
- Calibration of Cohorts of Virtual Patient Heart Models Using Bayesian History Matching.Annals of biomedical engineering · 2023Article
- Sensitivity analysis and inverse uncertainty quantification for the left ventricular passive mechanics.Biomechanics and modeling in mechanobiology · 2022Article
- Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle.International journal for numerical methods in biomedical engineering · 2022Article
- Efficient Ventricular Parameter Estimation Using AI-Surrogate Models.Frontiers in physiology · 2021Article
- Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium.Royal Society open science · 2021Article
- Uncertainty quantification and sensitivity analysis of left ventricular function during the full cardiac cycle.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
In recent years, we have witnessed substantial advances in the mathematical modelling of the biomechanical processes underlying the dynamics of the cardiac soft-tissue. Gao et al. (Gao et al. 2017 J. R. Soc. Interface 14, 20170203 ( doi:10.1098/rsif.2017.0203 )) demonstrated that the parameters underlying the biomechanical model have diagnostic value for prognosticating the risk of myocardial infarction. However, the computational costs of parameter estimation are prohibitive when the goal lies in building real-time clinical decision support systems. This is due to the need to repeatedly solve the mathematical equations numerically using finite-element discretization during an iterative optimization routine. The present article presents a method for accelerating the inference of the constitutive parameters by using statistical emulation with Gaussian processes. We demonstrate how the computational costs can be reduced by about three orders of magnitude, with hardly any loss in accuracy, and we assess various alternative techniques in a comparative evaluation study based on simulated data obtained by solving the left ventricular model with the finite-element method, and real magnetic resonance images data for a human volunteer.
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