Recent progress in machine learning (ML), together with advanced computational power,
have provided new research opportunities in cardiovascular modeling. While classifying …

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and
Machine Learning (ML) with cheminformatics has proven to be a powerful combination …

Computational fluid dynamics (CFD) modeling of blood flow plays an important role in better
understanding various medical conditions, designing more effective drug delivery systems …

Mathematical and computational modeling of the cardiovascular system is increasingly
providing non‐invasive alternatives to traditional invasive clinical procedures. Moreover, it …

Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and
analyse potential treatment options. CFD has shown to be beneficial in improving patient …

Aortic valve defects are among the most prevalent clinical conditions. A severely damaged
or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) …

Introduction The computational modelling of blood flow is known to provide vital
hemodynamic parameters for diagnosis and treatment-support for patients with valvular …

Abstract “Image-based” computational fluid dynamics (CFD) simulations provide insights
into each patient's hemodynamic environment. However, current standard procedures for …

An algorithm providing information on the key geometric features of an aorta extracted from
multi-slice computed tomography images is proposed. Using the numerical method, the …

Background and objective Recently, computational fluid dynamics enables the non-invasive
calculation of fractional flow reserve (FFR) based on 3D coronary model, but it is time …