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Models, Cardiovascular

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Hemodynamic study of blood flow in the aorta during the interventional robot treatment using fluid-structure interaction.

Biomechanics and modeling in mechanobiology
An interventional robot is a means for vascular diagnosis and treatment, and it can perform dredging, releasing drug and operating. Normal hemodynamic indicators are a prerequisite for the application of interventional robots. The current hemodynamic...

Comparing the performance of machine learning and conventional models for predicting atherosclerotic cardiovascular disease in a general Chinese population.

BMC medical informatics and decision making
BACKGROUND: Accurately predicting the risk of atherosclerotic cardiovascular disease (ASCVD) is crucial for implementing individualized prevention strategies and improving patient outcomes. Our objective is to develop machine learning (ML)-based mode...

Learning reduced-order models for cardiovascular simulations with graph neural networks.

Computers in biology and medicine
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 anatomies that contain numerous junctions or pathological conditions. We develop...

Deep-learning-based real-time individualization for reduce-order haemodynamic model.

Computers in biology and medicine
The reduced-order lumped parameter model (LPM) has great computational efficiency in real-time numerical simulations of haemodynamics but is limited by the accuracy of patient-specific computation. This study proposed a method to achieve the individu...

Exploring a new frontier in cardiac diagnosis: ECG analysis enhanced by machine learning and parametric quartic spline modeling.

Journal of electrocardiology
The heart's study holds paramount importance in human physiology, driving valuable research in cardiovascular health. However, assessing Electrocardiogram (ECG) analysis techniques poses challenges due to noise and artifacts in authentic recordings. ...

Image2Flow: A proof-of-concept hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data.

PLoS computational biology
Computational fluid dynamics (CFD) can be used for non-invasive evaluation of hemodynamics. However, its routine use is limited by labor-intensive manual segmentation, CFD mesh creation, and time-consuming simulation. This study aims to train a deep ...

Physics-informed neural networks for parameter estimation in blood flow models.

Computers in biology and medicine
BACKGROUND: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving inverse problems, especially in cases where no complete information about the system is known and scatter measurements are available. This is especially ...

Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials.

Cardiovascular engineering and technology
BACKGROUND AND OBJECTIVE: Advanced material models and material characterization of soft biological tissues play an essential role in pre-surgical planning for vascular surgeries and transcatheter interventions. Recent advances in heart valve enginee...

Modeling 3D Cardiac Contraction and Relaxation With Point Cloud Deformation Networks.

IEEE journal of biomedical and health informatics
Global single-valued biomarkers, such as ejection fraction, are widely used in clinical practice to assess cardiac function. However, they only approximate the heart's true 3D deformation process, thus limiting diagnostic accuracy and the understandi...

Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging. In this study, we propose novel alternatives to the traditional iVFM optimization scheme using physics-informed neural networks (PINNs) and a ...