Coronary artery decision algorithm trained by two-step machine learning algorithm

A two-step machine learning (ML) algorithm for estimating both fractional flow reserve (FFR) and decision (DEC) for the coronary artery is introduced in this study. The primary purpose of this model is to suggest the possibility of ML-based FFR to be more accurate than the FFR calculation technique...

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Autores principales: Kim, Young Woo, Yu, Hee-Jin, Kim, Jung-Sun, Ha, Jinyong, Choi, Jongeun, Lee, Joon Sang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9048707/
https://www.ncbi.nlm.nih.gov/pubmed/35492670
http://dx.doi.org/10.1039/c9ra08999c
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author Kim, Young Woo
Yu, Hee-Jin
Kim, Jung-Sun
Ha, Jinyong
Choi, Jongeun
Lee, Joon Sang
author_facet Kim, Young Woo
Yu, Hee-Jin
Kim, Jung-Sun
Ha, Jinyong
Choi, Jongeun
Lee, Joon Sang
author_sort Kim, Young Woo
collection PubMed
description A two-step machine learning (ML) algorithm for estimating both fractional flow reserve (FFR) and decision (DEC) for the coronary artery is introduced in this study. The primary purpose of this model is to suggest the possibility of ML-based FFR to be more accurate than the FFR calculation technique based on a computational fluid dynamics (CFD) method. For this purpose, a two-step ML algorithm that considers the flow characteristics and biometric features as input features of the ML model is designed. The first step of the algorithm is based on the Gaussian progress regression model and is trained by a synthetic model using CFD analysis. The second step of the algorithm is based on a support vector machine with patient data, including flow characteristics and biometric features. Consequently, the accuracy of the FFR estimated from the first step of the algorithm was similar to that of the CFD-based method, while the accuracy of DEC in the second step was improved. This improvement in accuracy was analyzed using flow characteristics and biometric features.
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spelling pubmed-90487072022-04-28 Coronary artery decision algorithm trained by two-step machine learning algorithm Kim, Young Woo Yu, Hee-Jin Kim, Jung-Sun Ha, Jinyong Choi, Jongeun Lee, Joon Sang RSC Adv Chemistry A two-step machine learning (ML) algorithm for estimating both fractional flow reserve (FFR) and decision (DEC) for the coronary artery is introduced in this study. The primary purpose of this model is to suggest the possibility of ML-based FFR to be more accurate than the FFR calculation technique based on a computational fluid dynamics (CFD) method. For this purpose, a two-step ML algorithm that considers the flow characteristics and biometric features as input features of the ML model is designed. The first step of the algorithm is based on the Gaussian progress regression model and is trained by a synthetic model using CFD analysis. The second step of the algorithm is based on a support vector machine with patient data, including flow characteristics and biometric features. Consequently, the accuracy of the FFR estimated from the first step of the algorithm was similar to that of the CFD-based method, while the accuracy of DEC in the second step was improved. This improvement in accuracy was analyzed using flow characteristics and biometric features. The Royal Society of Chemistry 2020-01-24 /pmc/articles/PMC9048707/ /pubmed/35492670 http://dx.doi.org/10.1039/c9ra08999c Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/
spellingShingle Chemistry
Kim, Young Woo
Yu, Hee-Jin
Kim, Jung-Sun
Ha, Jinyong
Choi, Jongeun
Lee, Joon Sang
Coronary artery decision algorithm trained by two-step machine learning algorithm
title Coronary artery decision algorithm trained by two-step machine learning algorithm
title_full Coronary artery decision algorithm trained by two-step machine learning algorithm
title_fullStr Coronary artery decision algorithm trained by two-step machine learning algorithm
title_full_unstemmed Coronary artery decision algorithm trained by two-step machine learning algorithm
title_short Coronary artery decision algorithm trained by two-step machine learning algorithm
title_sort coronary artery decision algorithm trained by two-step machine learning algorithm
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9048707/
https://www.ncbi.nlm.nih.gov/pubmed/35492670
http://dx.doi.org/10.1039/c9ra08999c
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