ArticleRoyal Society open science2021
Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium.
Article in Royal Society open science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Physics-Informed Emulation of Systemic Circulation for Fast Parameter Estimation and Uncertainty Quantification.International journal for numerical methods in biomedical engineering · 2026Article
- Developing cardiac biomechanical models beyond the clinic: modeling stressors of daily life.Biomechanics and modeling in mechanobiology · 2025Review
- Deep Learning-Based Estimation of Myocardial Material Parameters from Cardiac MRI.Bioengineering (Basel, Switzerland) · 2025Article
- Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations.NPJ digital medicine · 2024Article
- Deep Q-learning to globally optimize aQuantitative imaging in medicine and surgery · 2023Article
- Tendon Stress Estimation from Strain Data of a Bridge Girder Using Machine Learning-Based Surrogate Model.Sensors (Basel, Switzerland) · 2023Article
- AI-Guided Computing Insights into a Thermostat Monitoring Neonatal Intensive Care Unit (NICU).Sensors (Basel, Switzerland) · 2023Article
- Bridging the gap between mechanistic biological models and machine learning surrogates.PLoS computational biology · 2023Review
- Quantitative mapping of force-pCa curves to whole-heart contraction and relaxation.The Journal of physiology · 2022Article
- An Integrated Workflow for Building Digital Twins of Cardiac Electromechanics-A Multi-Fidelity Approach for Personalising Active Mechanics.Mathematics (Basel, Switzerland) · 2022Article
- A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion.Frontiers in bioengineering and biotechnology · 2021Article
- Efficient Ventricular Parameter Estimation Using AI-Surrogate Models.Frontiers in physiology · 2021Article
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6 authors.
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Abstract
A long-standing problem at the frontier of biomechanical studies is to develop fast methods capable of estimating material properties from clinical data. In this paper, we have studied three surrogate models based on machine learning (ML) methods for fast parameter estimation of left ventricular (LV) myocardium. We use three ML methods named K-nearest neighbour (KNN), XGBoost and multi-layer perceptron (MLP) to emulate the relationships between pressure and volume strains during the diastolic filling. Firstly, to train the surrogate models, a forward finite-element simulator of LV diastolic filling is used. Then the training data are projected in a low-dimensional parametrized space. Next, three ML models are trained to learn the relationships of pressure-volume and pressure-strain. Finally, an inverse parameter estimation problem is formulated by using those trained surrogate models. Our results show that the three ML models can learn the relationships of pressure-volume and pressure-strain very well, and the parameter inference using the surrogate models can be carried out in minutes. Estimated parameters from both the XGBoost and MLP models have much less uncertainties compared with the KNN model. Our results further suggest that the XGBoost model is better for predicting the LV diastolic dynamics and estimating passive parameters than other two surrogate models. Further studies are warranted to investigate how XGBoost can be used for emulating cardiac pump function in a multi-physics and multi-scale framework.
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