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A machine learning model to estimate myocardial stiffness from EDPVR
In-vivo estimation of mechanical properties of the myocardium is essential for patient-specific diagnosis and prognosis of cardiac disease involving myocardial remodeling, including myocardial infarction and heart failure with preserved ejection fraction. Current approaches use time-consuming finite...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8971532/ https://www.ncbi.nlm.nih.gov/pubmed/35361836 http://dx.doi.org/10.1038/s41598-022-09128-6 |
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author | Babaei, Hamed Mendiola, Emilio A. Neelakantan, Sunder Xiang, Qian Vang, Alexander Dixon, Richard A. F. Shah, Dipan J. Vanderslice, Peter Choudhary, Gaurav Avazmohammadi, Reza |
author_facet | Babaei, Hamed Mendiola, Emilio A. Neelakantan, Sunder Xiang, Qian Vang, Alexander Dixon, Richard A. F. Shah, Dipan J. Vanderslice, Peter Choudhary, Gaurav Avazmohammadi, Reza |
author_sort | Babaei, Hamed |
collection | PubMed |
description | In-vivo estimation of mechanical properties of the myocardium is essential for patient-specific diagnosis and prognosis of cardiac disease involving myocardial remodeling, including myocardial infarction and heart failure with preserved ejection fraction. Current approaches use time-consuming finite-element (FE) inverse methods that involve reconstructing and meshing the heart geometry, imposing measured loading, and conducting computationally expensive iterative FE simulations. In this paper, we propose a machine learning (ML) model that feasibly and accurately predicts passive myocardial properties directly from select geometric, architectural, and hemodynamic measures, thus bypassing exhaustive steps commonly required in cardiac FE inverse problems. Geometric and fiber-orientation features were chosen to be readily obtainable from standard cardiac imaging protocols. The end-diastolic pressure-volume relationship (EDPVR), which can be obtained using a single-point pressure-volume measurement, was used as a hemodynamic (loading) feature. A comprehensive ML training dataset in the geometry-architecture-loading space was generated, including a wide variety of partially synthesized rodent heart geometry and myofiber helicity possibilities, and a broad range of EDPVRs obtained using forward FE simulations. Latin hypercube sampling was used to create 2500 examples for training, validation, and testing. A multi-layer feed-forward neural network (MFNN) was used as a deep learning agent to train the ML model. The model showed excellent performance in predicting stiffness parameters [Formula: see text] and [Formula: see text] associated with fiber direction ([Formula: see text] and [Formula: see text] ). After conducting permutation feature importance analysis, the ML performance further improved for [Formula: see text] ([Formula: see text] ), and the left ventricular volume and endocardial area were found to be the most critical geometric features for accurate predictions. The ML model predictions were evaluated further in two cases: (i) rat-specific stiffness data measured using ex-vivo mechanical testing, and (ii) patient-specific estimation using FE inverse modeling. Excellent agreements with ML predictions were found for both cases. The trained ML model offers a feasible technology to estimate patient-specific myocardial properties, thus, bridging the gap between EDPVR, as a confounded organ-level metric for tissue stiffness, and intrinsic tissue-level properties. These properties provide incremental information relative to traditional organ-level indices for cardiac function, improving the clinical assessment and prognosis of cardiac diseases. |
format | Online Article Text |
id | pubmed-8971532 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-89715322022-04-05 A machine learning model to estimate myocardial stiffness from EDPVR Babaei, Hamed Mendiola, Emilio A. Neelakantan, Sunder Xiang, Qian Vang, Alexander Dixon, Richard A. F. Shah, Dipan J. Vanderslice, Peter Choudhary, Gaurav Avazmohammadi, Reza Sci Rep Article In-vivo estimation of mechanical properties of the myocardium is essential for patient-specific diagnosis and prognosis of cardiac disease involving myocardial remodeling, including myocardial infarction and heart failure with preserved ejection fraction. Current approaches use time-consuming finite-element (FE) inverse methods that involve reconstructing and meshing the heart geometry, imposing measured loading, and conducting computationally expensive iterative FE simulations. In this paper, we propose a machine learning (ML) model that feasibly and accurately predicts passive myocardial properties directly from select geometric, architectural, and hemodynamic measures, thus bypassing exhaustive steps commonly required in cardiac FE inverse problems. Geometric and fiber-orientation features were chosen to be readily obtainable from standard cardiac imaging protocols. The end-diastolic pressure-volume relationship (EDPVR), which can be obtained using a single-point pressure-volume measurement, was used as a hemodynamic (loading) feature. A comprehensive ML training dataset in the geometry-architecture-loading space was generated, including a wide variety of partially synthesized rodent heart geometry and myofiber helicity possibilities, and a broad range of EDPVRs obtained using forward FE simulations. Latin hypercube sampling was used to create 2500 examples for training, validation, and testing. A multi-layer feed-forward neural network (MFNN) was used as a deep learning agent to train the ML model. The model showed excellent performance in predicting stiffness parameters [Formula: see text] and [Formula: see text] associated with fiber direction ([Formula: see text] and [Formula: see text] ). After conducting permutation feature importance analysis, the ML performance further improved for [Formula: see text] ([Formula: see text] ), and the left ventricular volume and endocardial area were found to be the most critical geometric features for accurate predictions. The ML model predictions were evaluated further in two cases: (i) rat-specific stiffness data measured using ex-vivo mechanical testing, and (ii) patient-specific estimation using FE inverse modeling. Excellent agreements with ML predictions were found for both cases. The trained ML model offers a feasible technology to estimate patient-specific myocardial properties, thus, bridging the gap between EDPVR, as a confounded organ-level metric for tissue stiffness, and intrinsic tissue-level properties. These properties provide incremental information relative to traditional organ-level indices for cardiac function, improving the clinical assessment and prognosis of cardiac diseases. Nature Publishing Group UK 2022-03-31 /pmc/articles/PMC8971532/ /pubmed/35361836 http://dx.doi.org/10.1038/s41598-022-09128-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Babaei, Hamed Mendiola, Emilio A. Neelakantan, Sunder Xiang, Qian Vang, Alexander Dixon, Richard A. F. Shah, Dipan J. Vanderslice, Peter Choudhary, Gaurav Avazmohammadi, Reza A machine learning model to estimate myocardial stiffness from EDPVR |
title | A machine learning model to estimate myocardial stiffness from EDPVR |
title_full | A machine learning model to estimate myocardial stiffness from EDPVR |
title_fullStr | A machine learning model to estimate myocardial stiffness from EDPVR |
title_full_unstemmed | A machine learning model to estimate myocardial stiffness from EDPVR |
title_short | A machine learning model to estimate myocardial stiffness from EDPVR |
title_sort | machine learning model to estimate myocardial stiffness from edpvr |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8971532/ https://www.ncbi.nlm.nih.gov/pubmed/35361836 http://dx.doi.org/10.1038/s41598-022-09128-6 |
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