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Models, Cardiovascular

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Refined matrix completion for spectrum estimation of heart rate variability.

Mathematical biosciences and engineering : MBE
Heart rate variability (HRV) is an important metric in cardiovascular health monitoring. Spectral analysis of HRV provides essential insights into the functioning of the cardiac autonomic nervous system. However, data artefacts could degrade signal q...

Synergistic biophysics and machine learning modeling to rapidly predict cardiac growth probability.

Computers in biology and medicine
Computational models that can predict growth and remodeling of the heart could have important clinical applications. However, the time it takes to calibrate and run current models while considering data uncertainty and variability makes them impracti...

Rapid estimation of left ventricular contractility with a physics-informed neural network inverse modeling approach.

Artificial intelligence in medicine
Physics-based computer models based on numerical solutions of the governing equations generally cannot make rapid predictions, which in turn limits their applications in the clinic. To address this issue, we developed a physics-informed neural networ...

Digital phenotyping from heart rate dynamics: Identification of zero-poles models with data-driven evolutionary learning.

Computers in biology and medicine
Heart rate response to physical activity is widely investigated in clinical and training practice, as it provides information on a person's physical state. For emerging digital phenotyping approaches, there is a need for individualized model estimati...

Pinning down the accuracy of physics-informed neural networks under laminar and turbulent-like aortic blood flow conditions.

Computers in biology and medicine
BACKGROUND: Physics-informed neural networks (PINNs) are increasingly being used to model cardiovascular blood flow. The accuracy of PINNs is dependent on flow complexity and could deteriorate in the presence of highly-dynamical blood flow conditions...

Super-resolution left ventricular flow and pressure mapping by Navier-Stokes-informed neural networks.

Computers in biology and medicine
Intraventricular vector flow mapping (VFM) is an increasingly adopted echocardiographic technique that derives time-resolved two-dimensional flow maps in the left ventricle (LV) from color-Doppler sequences. Current VFM models rely on kinematic const...

AI-Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Aortic stenosis (AS) is the most common valvular heart disease in developed countries. High-fidelity preclinical models can improve AS management by enabling therapeutic innovation, early diagnosis, and tailored treatment planning. However, their use...

Active Machine Learning for Pre-procedural Prediction of Time-Varying Boundary Condition After Fontan Procedure Using Generative Adversarial Networks.

Annals of biomedical engineering
The Fontan procedure is the definitive palliation for pediatric patients born with single ventricles. Surgical planning for the Fontan procedure has emerged as a promising vehicle toward optimizing outcomes, where pre-operative measurements are used ...

Simulation-free prediction of atrial fibrillation inducibility with the fibrotic kernel signature.

Medical image analysis
Computational models of atrial fibrillation (AF) can help improve success rates of interventions, such as ablation. However, evaluating the efficacy of different treatments requires performing multiple costly simulations by pacing at different points...

Activation of a Soft Robotic Left Ventricular Phantom Embedded in a Closed-Loop Cardiovascular Simulator: A Computational and Experimental Analysis.

Cardiovascular engineering and technology
PURPOSE: Cardiovascular simulators are used in the preclinical testing phase of medical devices. Their reliability increases the more they resemble clinically relevant scenarios. In this study, a physiologically actuated soft robotic left ventricle (...