Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning
Cardiovascular diseases are the leading cause of mortality, morbidity, and hospitalization
around the world. Recent technological advances have facilitated analyzing, visualizing …
around the world. Recent technological advances have facilitated analyzing, visualizing …
Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology
Digital twins employ mathematical and computational models to virtually represent a
physical object (eg, planes and human organs), predict the behavior of the object, and …
physical object (eg, planes and human organs), predict the behavior of the object, and …
In situ expansion, differentiation, and electromechanical coupling of human cardiac muscle in a 3D bioprinted, chambered organoid
Rationale: One goal of cardiac tissue engineering is the generation of a living, human pump
in vitro that could replace animal models and eventually serve as an in vivo therapeutic …
in vitro that could replace animal models and eventually serve as an in vivo therapeutic …
Fluid–structure interaction simulations of patient-specific aortic dissection
Credible computational fluid dynamic (CFD) simulations of aortic dissection are challenging,
because the defining parallel flow channels—the true and the false lumen—are separated …
because the defining parallel flow channels—the true and the false lumen—are separated …
Physics-informed neural networks for brain hemodynamic predictions using medical imaging
Determining brain hemodynamics plays a critical role in the diagnosis and treatment of
various cerebrovascular diseases. In this work, we put forth a physics-informed deep …
various cerebrovascular diseases. In this work, we put forth a physics-informed deep …
Comparing different nonlinear dimensionality reduction techniques for data-driven unsteady fluid flow modeling
Computational fluid dynamics (CFD) is known for producing high-dimensional
spatiotemporal data. Recent advances in machine learning (ML) have introduced a myriad …
spatiotemporal data. Recent advances in machine learning (ML) have introduced a myriad …
[HTML][HTML] Patient-specific modeling of blood flow in the coronary arteries
Patient-specific models of blood flow in the coronary arteries have entered clinical practice
worldwide to aid in the diagnosis and management of heart disease. This technology …
worldwide to aid in the diagnosis and management of heart disease. This technology …
Learning reduced-order models for cardiovascular simulations with graph neural networks
L Pegolotti, MR Pfaller, NL Rubio, K Ding… - Computers in Biology …, 2024 - Elsevier
Reduced-order models based on physics are a popular choice in cardiovascular modeling
due to their efficiency, but they may experience loss in accuracy when working with …
due to their efficiency, but they may experience loss in accuracy when working with …
Hemodynamic modeling, medical imaging, and machine learning and their applications to cardiovascular interventions
Cardiovascular disease is a deadly global health crisis that carries a substantial financial
burden. Innovative treatment and management of cardiovascular disease straddles …
burden. Innovative treatment and management of cardiovascular disease straddles …
CRIMSON: An open-source software framework for cardiovascular integrated modelling and simulation
CJ Arthurs, R Khlebnikov, A Melville… - PLoS computational …, 2021 - journals.plos.org
In this work, we describe the CRIMSON (CardiovasculaR Integrated Modelling and
SimulatiON) software environment. CRIMSON provides a powerful, customizable and user …
SimulatiON) software environment. CRIMSON provides a powerful, customizable and user …