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%).

中文翻译：

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使用基于血管内超声图像的流固耦合模型和机器学习方法预测人类冠状动脉斑块易损性变化

摘要 斑块易损性预测在心血管研究中具有重要意义。体内随访血管内超声 (IVUS) 冠状动脉斑块数据从 9 名患者中获取，以构建流固耦合模型以获得斑块生物力学条件。定义了形态学斑块易损性指数（MPVI）来衡量斑块易损性。引入广义线性混合回归模型 (GLMM)、支持向量机 (SVM) 和随机森林 (RF) 以使用 10 个基线风险因素预测 MPVI 变化（ΔMPVI = MPVIfollow-up-MPVIbaseline）。平均壁厚、管腔面积、斑块面积、临界斑块壁应力和 MPVI 的组合是使用 RF 的最佳预测因子，其预测精度最高，为 91.47%，而 SVM 为 90.78%，GLMM 为 85.56%。机器学习方法 (RF) 将预测精度比 GLMM 提高了 5.91%。MPVI 是使用 GLMM (82.09%) 和 RF (78.53%) 的最佳单一风险因素，而使用 SVM (81.29%) 的斑块面积是最佳的。