Cai, L., Ren, L., Wang, Y., Xie, W., Zhi, G. and Gao, H. (2021) Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium. Royal Society Open Science, 8(1), 201121. (doi: 10.1098/rsos.201121)
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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.
| Item Type: | Articles |
|---|---|
| Keywords: | Parameter estimation, finite-element method, surrogate model, machine learning method, the inverse problem. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Gao, Dr Hao |
| Authors: | Cai, L., Ren, L., Wang, Y., Xie, W., Zhi, G., and Gao, H. |
| Subjects: | Q Science > QA Mathematics |
| College/School: | College of Science and Engineering > School of Mathematics and Statistics > Mathematics |
| Journal Name: | Royal Society Open Science |
| Publisher: | The Royal Society |
| ISSN: | 2054-5703 |
| ISSN (Online): | 2054-5703 |
| Published Online: | 13 January 2021 |
| Copyright Holders: | Copyright © 2021 The Authors |
| First Published: | First published in Royal Society Open Science 8(1): 201121 |
| Publisher Policy: | Reproduced under a Creative Commons License |
| Data DOI: | 10.5281/zenodo.4280222 |
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