Using intravascular ultrasound image-based fluid-structure interaction models and machine learning methods to predict human coronary plaque vulnerability change.

Journal: Computer methods in biomechanics and biomedical engineering
Published Date: Jul 22, 2020

Abstract

Plaque vulnerability prediction is of great importance in cardiovascular research. In vivo follow-up intravascular ultrasound (IVUS) coronary plaque data were acquired from nine patients to construct fluid-structure interaction models to obtain plaque biomechanical conditions. Morphological plaque vulnerability index (MPVI) was defined to measure plaque vulnerability. The generalized linear mixed regression model (GLMM), support vector machine (SVM) and random forest (RF) were introduced to predict MPVI change (ΔMPVI = MPVI‒MPVI) using ten risk factors at baseline. The combination of mean wall thickness, lumen area, plaque area, critical plaque wall stress, and MPVI was the best predictor using RF with the highest prediction accuracy 91.47%, compared to 90.78% from SVM, and 85.56% from GLMM. Machine learning method (RF) improved the prediction accuracy by 5.91% over that from GLMM. MPVI was the best single risk factor using both GLMM (82.09%) and RF (78.53%) while plaque area was the best using SVM (81.29%).

Authors

  • Liang Wang
    Information Department, Dazhou Central Hospital, Dazhou 635000, China.
  • Dalin Tang
    School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
  • Akiko Maehara
    Clinical Trial Center, Cardiovascular Research Foundation, New York, New York, USA.
  • Zheyang Wu
    Department of Mathematical Sciences, Worcester Polytechnic Institute, Worcester, MA, United States of America.
  • Chun Yang
    State Key Laboratory of Biogeology and Environmental Geology, School of Earth Sciences, China University of Geosciences, Wuhan, 430074, China.
  • David Muccigrosso
    Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA.
  • Mitsuaki Matsumura
    Clinical Trial Center, Cardiovascular Research Foundation, New York, New York, USA.
  • Jie Zheng
    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
  • Richard Bach
    Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA.
  • Kristen L Billiar
    Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.
  • Gregg W Stone
  • Gary S Mintz
    Clinical Trial Center, Cardiovascular Research Foundation, New York, New York, USA.

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