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

机译：两步机学习算法训练冠状动脉决策算法

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摘要

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.
机译：本研究介绍了一种用于估算冠状动脉的分数流量储备（FFR）和决策（DEC）的两步机学习（ML）算法。该模型的主要目的是提出基于ML的FFR的可能性比基于计算流体动力学（CFD）方法更准确于FFR计算技术。为此目的，设计了一种两步ML算法，其认为流量特性和生物识别功能作为ML型号的输入特征。该算法的第一步是基于高斯进展回归模型，并通过使用CFD分析通过合成模型训练。该算法的第二步骤基于具有患者数据的支持向量机，包括流动特性和生物特征。因此，从算法的第一步估计的FFR的准确性类似于基于CFD的方法的FFR，而第二步中DEC的精度得到改善。使用流动特性和生物识别特征分析了这种准确性的改进。

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  • 来源
    《RSC Advances》 |2020年第7期|共9页
  • 作者

    Kim Young Woo; Yu Hee-Jin; Kim Jung-Sun; Ha Jinyong; Choi Jongeun; Lee Joon Sang;

  • 作者单位

    Yonsei Univ Dept Mech Engn Seoul South Korea;

    Yonsei Univ Dept Mech Engn Seoul South Korea;

    Yonsei Univ Div Cardiol Severance Cardiovasc Hosp Coll Med Seoul South Korea;

    Sejong Univ Dept Elect Engn Seoul South Korea;

    Yonsei Univ Dept Mech Engn Seoul South Korea;

    Yonsei Univ Dept Mech Engn Seoul South Korea;

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  • 正文语种 eng
  • 中图分类 化学;
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