Data-Driven Inversion of Hemodynamic Parameters for Combined Stenotic Left Coronary Artery Aneurysms
10.16156/j.1004-7220.2024.05.009
- VernacularTitle:数据驱动下合并狭窄左冠状动脉瘤血流动力学参数反演方法
- Author:
Zhengjia SHI
1
;
Lifang SUN
;
Mingxuan ZHAO
;
Mengqiang JI
;
Yulong SHI
;
Jianbing SANG
Author Information
1. 河北工业大学 机械工程学院,天津 300401
- Keywords:
aneurysm;
numerical simulation;
machine learning;
deep learning;
random forest;
hemodynamics
- From:
Journal of Medical Biomechanics
2024;39(5):853-859
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the application of machine learning to predict the hemodynamic parameters of combined stenotic left coronary artery(LCA)aneurysms.Methods Parameterized modeling and simulation based on the geometric parameter range of combined stenosis LCA aneurysms in clinical statistics were conducted.The obtained simulation data was used as the dataset,and two common machine learning models were constructed and trained for optimization to predict two key hemodynamic parameters:wall shear stress(WSS)and pressure.By comparing and analyzing the performances of these models on the training and testing sets,the accuracy of each model was evaluated,and the effectiveness of the data-driven prediction of hemodynamic parameters for LCA aneurysms with concomitant stenosis was verified.Results The effectiveness of machine learning method in inverting the hemodynamic parameters of aneurysms was determined.For WSS prediction,the trained deep learning model and random forest model achieved mean squared error(MSE),mean absolute error(MAE),and determination coefficient R2 of 0.052 8,0.032 2,0.988 3,and 0.078 2,0.046 3,and 0.976 6,respectively.For pressure prediction,the accuracies of the deep learning models and random forest models were comparable,with MSE,MAE,and R2 of 4.67×10-6,3×10-4,0.999 7,and 1.07×10-5,5×10-4,and 0.999 3,respectively.Conclusions Machine learning methods show high accuracy in predicting the hemodynamic parameters of combined stenotic coronary artery aneurysm models.The predictive accuracy of the model,computational efficiency,and needs of the application scenarios need to be considered in machine learning prediction so that the appropriate model can be selected according to the specific situation.This study has clinical significance,helping doctors to more accurately evaluate a patient's condition and provide new ideas and method for the diagnosis and treatment of cardiovascular diseases.
