ArticleFrontiers in physics2019
Prediction of Left Ventricular Mechanics Using Machine Learning.
Article in Frontiers in physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Rapid prediction of cardiac activation in the left ventricle with geometric deep learning: a step towards cardiac resynchronization therapy planning.NPJ digital medicine · 2026Article
- Finite Element Modeling in Left Ventricular Cardiac Biomechanics: From Computational Tool to Clinical Practice.Bioengineering (Basel, Switzerland) · 2025Review
- Deep Learning-Based Estimation of Myocardial Material Parameters from Cardiac MRI.Bioengineering (Basel, Switzerland) · 2025Article
- Rapid estimation of left ventricular contractility with a physics-informed neural network inverse modeling approach.Artificial intelligence in medicine · 2024Article
- Real-time prediction of postoperative spinal shape with machine learning models trained on finite element biomechanical simulations.International journal of computer assisted radiology and surgery · 2024Article
- Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials.Cardiovascular engineering and technology · 2024Review
- Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations.NPJ digital medicine · 2024Article
- The Potential of Deep Learning to Advance Clinical Applications of Computational Biomechanics.Bioengineering (Basel, Switzerland) · 2023Review
- Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents.Scientific reports · 2023Article
- Bridging the gap between mechanistic biological models and machine learning surrogates.PLoS computational biology · 2023Review
- Survival Outcome of Gastric Signet Ring Cell Carcinoma Based on the Optimal Number of Examined Lymph Nodes: A Nomogram- and Machine-Learning-Based Approach.Journal of clinical medicine · 2023Article
- Machine learning used for simulation of MitraClip intervention: A proof-of-concept study.Frontiers in genetics · 2023Article
- Data-driven Modeling of the Mechanical Behavior of Anisotropic Soft Biological Tissue.Engineering with computers · 2022Article
- A Computationally Efficient Approach to Simulate Heart Rate Effects Using a Whole Human Heart Model.Bioengineering (Basel, Switzerland) · 2022Article
- A machine learning model to estimate myocardial stiffness from EDPVR.Scientific reports · 2022Article
- Predicting Overall Survival in Patients with Nonmetastatic Gastric Signet Ring Cell Carcinoma: A Machine Learning Approach.Computational and mathematical methods in medicine · 2022Article
- Predictive constitutive modelling of arteries by deep learning.Journal of the Royal Society, Interface · 2021Article
- Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes.Frontiers in cardiovascular medicine · 2021Article
- A Multiple Step Active Stiffness Integration Scheme to Couple a Stochastic Cross-Bridge Model and Continuum Mechanics for Uses in Both Basic Research and Clinical Applications of Heart Simulation.Frontiers in physiology · 2021Article
- Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium.Royal Society open science · 2021Article
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Authors and funding
6 authors.
Funding
Abstract
The goal of this paper was to provide a real-time left ventricular (LV) mechanics simulator using machine learning (ML). Finite element (FE) simulations were conducted for the LV with different material properties to obtain a training set. A hyperelastic fiber-reinforced material model was used to describe the passive behavior of the myocardium during diastole. The active behavior of the heart resulting from myofiber contractions was added to the passive tissue during systole. The active and passive properties govern the LV constitutive equation. These mechanical properties were altered using optimal Latin hypercube design of experiments to obtain training FE models with varied active properties (volume and pressure predictions) and varied passive properties (stress predictions). For prediction of LV pressures, we used eXtreme Gradient Boosting (XGboost) and Cubist, and XGBoost was used for predictions of LV pressures, volumes as well as LV stresses. The LV pressure and volume results obtained from ML were similar to FE computations. The ML results could capture the shape of LV pressure as well as LV pressure-volume loops. The results predicted by Cubist were smoother than those from XGBoost. The mean absolute errors were as follows: XGBoost volume: 1.734 ± 0.584 ml, XGBoost pressure: 1.544 ± 0.298 mmHg, Cubist volume: 1.495 ± 0.260 ml, Cubist pressure: 1.623 ± 0.191 mmHg, myofiber stress: 0.334 ± 0.228 kPa, cross myofiber stress: 0.075 ± 0.024 kPa, and shear stress: 0.050 ± 0.032 kPa. The simulation results show ML can predict LV mechanics much faster than the FE method. The ML model can be used as a tool to predict LV behavior. Training of our ML model based on a large group of subjects can improve its predictability for real world applications.
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