Advances in Hemodynamic Computation Based on Deep Learning
10.16156/j.1004-7220.2025.05.035
- VernacularTitle:基于深度学习的血流动力学计算研究进展
- Author:
Chunhao TAO
1
;
Luxin WANG
;
Aike QIAO
Author Information
1. 北京工业大学化学与生命科学学院,北京 100124;智能化生理测量与临床转化北京市国际科研合作基地,北京 100124
- Publication Type:Journal Article
- Keywords:
deep learning;
hemodynamics;
computational fluid dynamics;
artificial intelligence;
computer-aided diagnosis
- From:
Journal of Medical Biomechanics
2025;40(5):1354-1359
- CountryChina
- Language:Chinese
-
Abstract:
Cardiovascular diseases are the leading cause of death worldwide,and hemodynamics plays a significant role in understanding the mechanisms of these diseases,predicting disease progression,and guiding treatment strategies.Traditional methods for obtaining personalized hemodynamic parameters in clinical settings have numerous limitations,while the rise of deep learning technology has brought new opportunities for their computation.This review focuses on the application of deep learning in obtaining hemodynamic parameters in clinical settings,covering its progress in computational fluid dynamics preprocessing,hemodynamic computation(data-driven and PINN method),and magnetic resonance anagiography.It analyzes the advantages and challenges of each method and discusses future development directions,aiming to provide a reference for research on obtaining hemodynamic parameters in clinical settings using artificial intelligence method.
