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Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery
In this work, we integrated finite element (FE) method and machine learning (ML) method to predict the stent expansion in a calcified coronary artery. The stenting procedure was captured in a patient-specific artery model, reconstructed based on optical coherence tomography images. Following FE simu...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9328568/ https://www.ncbi.nlm.nih.gov/pubmed/35903558 http://dx.doi.org/10.3390/app10175820 |
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author | Dong, Pengfei Ye, Guochang Kaya, Mehmet Gu, Linxia |
author_facet | Dong, Pengfei Ye, Guochang Kaya, Mehmet Gu, Linxia |
author_sort | Dong, Pengfei |
collection | PubMed |
description | In this work, we integrated finite element (FE) method and machine learning (ML) method to predict the stent expansion in a calcified coronary artery. The stenting procedure was captured in a patient-specific artery model, reconstructed based on optical coherence tomography images. Following FE simulation, eight geometrical features in each of 120 cross sections in the pre-stenting artery model, as well as the corresponding post-stenting lumen area, were extracted for training and testing the ML models. A linear regression model and a support vector regression (SVR) model with three different kernels (linear, polynomial, and radial basis function kernels) were adopted in this work. Two subgroups of the eight features, i.e., stretch features and calcification features, were further assessed for the prediction capacity. The influence of the neighboring cross sections on the prediction accuracy was also investigated by averaging each feature over eight neighboring cross sections. Results showed that the SVR models provided better predictions than the linear regression model in terms of bias. In addition, the inclusion of stretch features based on mechanistic understanding could provide a better prediction, compared with the calcification features only. However, there were no statistically significant differences between neighboring cross sections and individual ones in terms of the prediction bias and range of error. The simulation-driven machine learning framework in this work could enhance the mechanistic understanding of stenting in calcified coronary artery lesions, and also pave the way toward precise prediction of stent expansion. |
format | Online Article Text |
id | pubmed-9328568 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-93285682022-07-27 Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery Dong, Pengfei Ye, Guochang Kaya, Mehmet Gu, Linxia Appl Sci (Basel) Article In this work, we integrated finite element (FE) method and machine learning (ML) method to predict the stent expansion in a calcified coronary artery. The stenting procedure was captured in a patient-specific artery model, reconstructed based on optical coherence tomography images. Following FE simulation, eight geometrical features in each of 120 cross sections in the pre-stenting artery model, as well as the corresponding post-stenting lumen area, were extracted for training and testing the ML models. A linear regression model and a support vector regression (SVR) model with three different kernels (linear, polynomial, and radial basis function kernels) were adopted in this work. Two subgroups of the eight features, i.e., stretch features and calcification features, were further assessed for the prediction capacity. The influence of the neighboring cross sections on the prediction accuracy was also investigated by averaging each feature over eight neighboring cross sections. Results showed that the SVR models provided better predictions than the linear regression model in terms of bias. In addition, the inclusion of stretch features based on mechanistic understanding could provide a better prediction, compared with the calcification features only. However, there were no statistically significant differences between neighboring cross sections and individual ones in terms of the prediction bias and range of error. The simulation-driven machine learning framework in this work could enhance the mechanistic understanding of stenting in calcified coronary artery lesions, and also pave the way toward precise prediction of stent expansion. 2020-09-01 2020-08-22 /pmc/articles/PMC9328568/ /pubmed/35903558 http://dx.doi.org/10.3390/app10175820 Text en https://creativecommons.org/licenses/by/4.0/This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Dong, Pengfei Ye, Guochang Kaya, Mehmet Gu, Linxia Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery |
title | Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery |
title_full | Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery |
title_fullStr | Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery |
title_full_unstemmed | Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery |
title_short | Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery |
title_sort | simulation-driven machine learning for predicting stent expansion in calcified coronary artery |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9328568/ https://www.ncbi.nlm.nih.gov/pubmed/35903558 http://dx.doi.org/10.3390/app10175820 |
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