Computer methods and programs in biomedicine
Jul 8, 2024
BACKGROUND AND OBJECTIVE: Numerical simulations in electrocardiology are often affected by various uncertainties inherited from the lack of precise knowledge regarding input values including those related to the cardiac cell model, domain geometry, a...
Cardiovascular engineering and technology
Jul 2, 2024
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...
The Journal of thoracic and cardiovascular surgery
Jun 29, 2024
OBJECTIVE: Aortic root replacement requires construction of a composite valve-graft and reimplantation of coronary arteries. This study assessed the feasibility of valve-in-valve transcatheter aortic valve implantation after aortic root replacement.
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 ...
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 ...
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. ...
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...
Computer methods in biomechanics and biomedical engineering
Jan 25, 2024
Considering the differences between individuals, in this paper, an uncertainty analysis model for predicting rupture risk of atherosclerotic arteries is established based on a back-propagation artificial neural network. The influence of isotropy and ...
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...
BMC medical informatics and decision making
Jul 24, 2023
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...
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