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

Authors
 Young Woo Kim  ;  Hee-Jin Yu  ;  Jung-Sun Kim  ;  Jinyong Ha  ;  Jongeun Choi  ;  Joon Sang Lee 
Citation
 RSC ADVANCES, Vol.10(7) : 4014-4022, 2020-01 
Journal Title
RSC ADVANCES
Issue Date
2020-01
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.
Files in This Item:
T9992020502.pdf Download
DOI
10.1039/c9ra08999c
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Jung Sun(김중선) ORCID logo https://orcid.org/0000-0003-2263-3274
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/190277
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