This paper represents a detailed and systematic review of one of the most ongoing applications of computational fluid dynamics(CFD)in biomedical applications.Beyond its various engineering applications,CFD has started...This paper represents a detailed and systematic review of one of the most ongoing applications of computational fluid dynamics(CFD)in biomedical applications.Beyond its various engineering applications,CFD has started to establish a presence in the biomedical field.Cardiac abnormality,a familiar health issue,is an essential point of investigation by research analysts.Diagnostic modalities provide cardiovascular structural information but give insufficient information about the hemodynamics of blood.The study of hemodynamic parameters can be a potential measure for determining cardiovascular abnormalities.Numerous studies have explored the rheological behavior of blood experimentally and numerically.This paper provides insight into how researchers have incorporated the pulsatile nature of the blood experimentally,numerically,or through various simulations over the years.It focuses on how machine learning platforms derive outputs based on mass and momentum conservation to predict the velocity and pressure profile,analyzing various cardiac diseases for clinical applications.This will pave the way toward responsive AI in cardiac healthcare,improving productivity and quality in the healthcare industry.The paper shows how CFD is a vital tool for efficiently studying the flow in arteries.The review indicates this biomedical simulation and its applications in healthcare using machine learning and AI.Developing AI-based CFD models can impact society and foster the advancement towards responsive AI.展开更多
文摘This paper represents a detailed and systematic review of one of the most ongoing applications of computational fluid dynamics(CFD)in biomedical applications.Beyond its various engineering applications,CFD has started to establish a presence in the biomedical field.Cardiac abnormality,a familiar health issue,is an essential point of investigation by research analysts.Diagnostic modalities provide cardiovascular structural information but give insufficient information about the hemodynamics of blood.The study of hemodynamic parameters can be a potential measure for determining cardiovascular abnormalities.Numerous studies have explored the rheological behavior of blood experimentally and numerically.This paper provides insight into how researchers have incorporated the pulsatile nature of the blood experimentally,numerically,or through various simulations over the years.It focuses on how machine learning platforms derive outputs based on mass and momentum conservation to predict the velocity and pressure profile,analyzing various cardiac diseases for clinical applications.This will pave the way toward responsive AI in cardiac healthcare,improving productivity and quality in the healthcare industry.The paper shows how CFD is a vital tool for efficiently studying the flow in arteries.The review indicates this biomedical simulation and its applications in healthcare using machine learning and AI.Developing AI-based CFD models can impact society and foster the advancement towards responsive AI.
