AIMC Topic: Models, Cardiovascular

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Optimization of hemocompatibility metrics in ventricular assist device design using machine learning and CFD-based response surface analysis.

The International journal of artificial organs
Ventricular assist devices (VADs) are essential for end-stage heart failure patients, but their design must balance hydraulic efficiency and hemocompatibility to minimize blood damage. This study presents a multi-objective optimization framework inte...

Multiscale simulations that incorporate patient-specific neural network models of platelet calcium signaling predict diverse thrombotic outcomes under flow.

PLoS computational biology
During thrombosis, platelets rapidly deposit and activate on the vessel wall, driving conditions such as myocardial infarction and stroke. The complexity of thrombus formation in pathological flow geometries, along with patient-specific pharmacologic...

Integrating anatomy and electrophysiology in the healthy human heart: Insights from biventricular statistical shape analysis using universal coordinates.

Computers in biology and medicine
A cardiac digital twin is a virtual replica of a patient-specific heart, mimicking its anatomy and physiology. A crucial step of building a cardiac digital twin is anatomical twinning, where the computational mesh of the digital twin is tailored to t...

Research on noninvasive electrophysiologic imaging based on cardiac electrophysiology simulation and deep learning methods for the inverse problem.

BMC cardiovascular disorders
BACKGROUND: The risk stratification and prognosis of cardiac arrhythmia depend on the individual condition of patients, while invasive diagnostic methods may be risky to patient health, and current non-invasive diagnostic methods are applicable to fe...

InVAErt networks for amortized inference and identifiability analysis of lumped-parameter haemodynamic models.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Estimation of cardiovascular model parameters from electronic health records (EHRs) poses a significant challenge primarily due to lack of identifiability. Structural non-identifiability arises when a manifold in the space of parameters is mapped to ...

Role of physics-informed constraints in real-time estimation of 3D vascular fluid dynamics using multi-case neural network.

Computers in biology and medicine
Numerical simulations of fluid dynamics in tube-like structures are important to biomedical research to model flow in blood vessels and airways. It is further useful to some clinical applications, such as predicting arterial fractional flow reserves,...

CardiOT: Towards Interpretable Drug Cardiotoxicity Prediction Using Optimal Transport and Kolmogorov--Arnold Networks.

IEEE journal of biomedical and health informatics
Investigating the inhibitory effects of compounds on cardiac ion channels is essential for assessing cardiac drug safety. Consequently, researchers have developed computational models to evaluate combined cardiotoxicity (CCT) on cardiac ion channels....

Constitutive neural networks for main pulmonary arteries: discovering the undiscovered.

Biomechanics and modeling in mechanobiology
Accurate modeling of cardiovascular tissues is crucial for understanding and predicting their behavior in various physiological and pathological conditions. In this study, we specifically focus on the pulmonary artery in the context of the Ross proce...

Rapid wall shear stress prediction for aortic aneurysms using deep learning: a fast alternative to CFD.

Medical & biological engineering & computing
Aortic aneurysms pose a significant risk of rupture. Previous research has shown that areas exposed to low wall shear stress (WSS) are more prone to rupture. Therefore, precise WSS determination on the aneurysm is crucial for rupture risk assessment....