January 13, 2026
The proposed computational framework provides a powerful tool for investigating HFrEF progression and electromechanical dysfunction. By accurately replicating pressure-volume loop characteristics and
November 1, 2025
This study presents a paradigm case of how to build virtual human models and digital twins by productive collaboration between teams with complementary
October 18, 2025
This study introduces a novel computational framework that integrates circadian hormonal fluctuations into virtual placebo groups, enhancing the physiological realism of cardiac safety simulations.
August 1, 2025
Big AI merges physics-based digital twins and data-driven intelligence to deliver truly personalised medicine.It unites interpretability and speed, enabling accurate, individualised predictions from
April 1, 2025
The purpose of our study is to describe the feasibility of a computational fluid dynamics model to predict hepatic artery flow and its variations following gastroduodenal (GDA) or common hepatic
February 18, 2025
The finite element method is widely used for studying the intervertebral disc at the organ level due to its ability to model complex geometries. An indispensable requirement for proper modelling
December 26, 2024
Accurate assessment of drug-induced proarrhythmic riskusing sex-specific cardiac
December 25, 2024
The objectives of our work are to create computational models of electrophysiology of human cardiac anatomies with a range of detail, reproduce a LBBB population of patients with and without CRT
October 28, 2024
This study presents a proof-of-principle pipeline to build personalized digital cardiac twins by integrating cardiovascular MRI and electrocardiographic imaging, demonstrating how engineers,
April 18, 2024
A recent paradigm shift in proarrhythmic risk assessment suggests that the integration of clinical, non-clinical, and computational evidence can be used to reach a comprehensive understanding of the
April 18, 2024
This study represents a significant step towards integrating in silico technologies into real clinical contexts, providing a robust framework for improving aortic stenosis diagnosis and the design