ArticleFrontiers in physiology2018
Gaussian Process Regressions for Inverse Problems and Parameter Searches in Models of Ventricular Mechanics.
Article in Frontiers in physiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Sensitivity analysis of factors in a microfluidics CFD model of coagulation and cardiac applications.Biomechanics and modeling in mechanobiology · 2026Article
- Bayesian parameter inference and uncertainty quantification for a computational pulmonary hemodynamics model using Gaussian processes.Computers in biology and medicine · 2025Article
- Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations.NPJ digital medicine · 2024Article
- The dependency of fetal left ventricular biomechanics function on myocardium helix angle configuration.Biomechanics and modeling in mechanobiology · 2023Article
- Bridging the gap between mechanistic biological models and machine learning surrogates.PLoS computational biology · 2023Review
- Analysis of noisy transient signals based on Gaussian process regression.Biophysical journal · 2023Article
- Strain energy density as a Gaussian process and its utilization in stochastic finite element analysis: application to planar soft tissues.Computer methods in applied mechanics and engineering · 2023Article
- 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
- Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data.Frontiers in physiology · 2021Article
- Application of feed forward and recurrent neural networks in simulation of left ventricular mechanics.Scientific reports · 2020Article
- Uncertainty quantification and sensitivity analysis of left ventricular function during the full cardiac cycle.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Article
- Article
- Fast parameter inference in a biomechanical model of the left ventricle by using statistical emulation.Journal of the Royal Statistical Society. Series C, Applied statistics · 2019Article
- Prediction of Left Ventricular Mechanics Using Machine Learning.Frontiers in physics · 2019Article
- Gaussian process emulation to accelerate parameter estimation in a mechanical model of the left ventricle: a critical step towards clinical end-user relevance.Journal of the Royal Society, Interface · 2019Article
- Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification.Computer methods in applied mechanics and engineering · 2019Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Patient specific models of ventricular mechanics require the optimization of their many parameters under the uncertainties associated with imaging of cardiac function. We present a strategy to reduce the complexity of parametric searches for 3-D FE models of left ventricular contraction. The study employs automatic image segmentation and analysis of an image database to gain geometric features for several classes of patients. Statistical distributions of geometric parameters are then used to design parametric studies investigating the effects of: (1) passive material properties during ventricular filling, and (2) infarct geometry on ventricular contraction in patients after a heart attack. Gaussian Process regression is used in both cases to build statistical models trained on the results of biophysical FEM simulations. The first statistical model estimates unloaded configurations based on either the intraventricular pressure or the end-diastolic fiber strain. The technique provides an alternative to the standard fixed-point iteration algorithm, which is more computationally expensive when used to unload more than 10 ventricles. The second statistical model captures the effects of varying infarct geometries on cardiac output. For training, we designed high resolution models of non-transmural infarcts including refinements of the border zone around the lesion. This study is a first effort in developing a platform combining HPC models and machine learning to investigate cardiac function in heart failure patients with the goal of assisting clinical diagnostics.
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