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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...

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Autores principales: Dong, Pengfei, Ye, Guochang, Kaya, Mehmet, Gu, Linxia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
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.
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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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