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

Computer Methods in Biomechanics and Biomedical Engineering
Liang WangGary S Mintz

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 = MPVIfollow-up‒MPVIbaseline) 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%).

References

May 16, 2000·Arteriosclerosis, Thrombosis, and Vascular Biology·R VirmaniS M Schwartz
Jul 3, 2007·American Journal of Physiology. Heart and Circulatory Physiology·Jacques OhayonGérard Finet
Feb 9, 2008·Journal of Biomechanics·Danny BluesteinShmuel Einav
Jan 21, 2011·The New England Journal of Medicine·Gregg W StoneUNKNOWN PROSPECT Investigators
Feb 15, 2014·Journal of Biomedical Optics·Lambros S AthanasiouDimitrios I Fotiadis

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Methods Mentioned

BETA
X-ray

Software Mentioned

MPVI
GLMM
TreeBagger
R
glmmPQL
MATLAB
ADINA

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