Coronary artery decision algorithm trained by two-step machine learning algorithm
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Young Woo | - |
dc.contributor.author | Yu, Hee-Jin | - |
dc.contributor.author | Kim, Jung-Sun | - |
dc.contributor.author | Ha, Jinyong | - |
dc.contributor.author | Choi, Jongeun | - |
dc.contributor.author | Lee, Joon Sang | - |
dc.date.accessioned | 2023-04-21T01:40:18Z | - |
dc.date.available | 2023-04-21T01:40:18Z | - |
dc.date.issued | 2020-01 | - |
dc.identifier.issn | 2046-2069 | - |
dc.identifier.issn | 2046-2069 | - |
dc.identifier.uri | https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/6613 | - |
dc.description.abstract | 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. | - |
dc.format.extent | 9 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | ROYAL SOC CHEMISTRY | - |
dc.title | Coronary artery decision algorithm trained by two-step machine learning algorithm | - |
dc.type | Article | - |
dc.publisher.location | 영국 | - |
dc.identifier.doi | 10.1039/c9ra08999c | - |
dc.identifier.scopusid | 2-s2.0-85078703542 | - |
dc.identifier.wosid | 000509900800047 | - |
dc.identifier.bibliographicCitation | RSC ADVANCES, v.10, no.7, pp 4014 - 4022 | - |
dc.citation.title | RSC ADVANCES | - |
dc.citation.volume | 10 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 4014 | - |
dc.citation.endPage | 4022 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.subject.keywordPlus | FRACTIONAL FLOW RESERVE | - |
dc.subject.keywordPlus | DIAGNOSTIC PERFORMANCE | - |
dc.subject.keywordPlus | COMPUTED-TOMOGRAPHY | - |
dc.subject.keywordPlus | FEATURE-SELECTION | - |
dc.subject.keywordPlus | ANGIOGRAPHY | - |
dc.subject.keywordPlus | SVM | - |
dc.subject.keywordPlus | QUANTIFICATION | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | MIGRATION | - |
dc.subject.keywordPlus | SEVERITY | - |
dc.subject.keywordAuthor | Coronary Artery Decision Algorithm | - |
dc.subject.keywordAuthor | Two-Step Machine Learning Algorithm | - |
dc.subject.keywordAuthor | fractional flow reserve | - |
dc.subject.keywordAuthor | lattice Boltzmann method | - |
dc.subject.keywordAuthor | hemodynamics | - |
Items in Scholar Hub are protected by copyright, with all rights reserved, unless otherwise indicated.
Yonsei University 50 Yonsei-ro Seodaemun-gu, Seoul, 03722, Republic of Korea1599-1885
© 2021 YONSEI UNIV. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.