AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Models, Cardiovascular

Showing 21 to 30 of 146 articles

Clear Filters

Noninvasive and fast method of calculation for instantaneous wave-free ratio based on haemodynamics and deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Instantaneous wave-free ratio (iFR) is a new invasive indicator of myocardial ischaemia, and its diagnostic performance is as good as the "gold standard" of myocardial ischaemia diagnosis: fractional flow reserve (FFR). iFR...

Unsupervised stochastic learning and reduced order modeling for global sensitivity analysis in cardiac electrophysiology models.

Computer methods and programs in biomedicine
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...

Reduced order modelling of intracranial aneurysm flow using proper orthogonal decomposition and neural networks.

International journal for numerical methods in biomedical engineering
Reduced order modelling (ROMs) methods, such as proper orthogonal decomposition (POD), systematically reduce the dimensionality of high-fidelity computational models and potentially achieve large gains in execution speed. Machine learning (ML) using ...

SDF4CHD: Generative modeling of cardiac anatomies with congenital heart defects.

Medical image analysis
Congenital heart disease (CHD) encompasses a spectrum of cardiovascular structural abnormalities, often requiring customized treatment plans for individual patients. Computational modeling and analysis of these unique cardiac anatomies can improve di...

Characterization of cardiac resynchronization therapy response through machine learning and personalized models.

Computers in biology and medicine
INTRODUCTION: The characterization and selection of heart failure (HF) patients for cardiac resynchronization therapy (CRT) remain challenging, with around 30% non-responder rate despite following current guidelines. This study aims to propose a nove...

Assessment of left ventricular wall thickness and dimension: accuracy of a deep learning model with prediction uncertainty.

The international journal of cardiovascular imaging
Left ventricular (LV) geometric patterns aid clinicians in the diagnosis and prognostication of various cardiomyopathies. The aim of this study is to assess the accuracy and reproducibility of LV dimensions and wall thickness using deep learning (DL)...

Predicting coronary artery occlusion risk from noninvasive images by combining CFD-FSI, cGAN and CNN.

Scientific reports
Wall Shear Stress (WSS) is one of the most important parameters used in cardiovascular fluid mechanics, and it provides a lot of information like the risk level caused by any vascular occlusion. Since WSS cannot be measured directly and other availab...

Physics-Informed Graph Neural Networks to solve 1-D equations of blood flow.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Computational models of hemodynamics can contribute to optimizing surgical plans, and improve our understanding of cardiovascular diseases. Recently, machine learning methods have become essential to reduce the computational...

SeqSeg: Learning Local Segments for Automatic Vascular Model Construction.

Annals of biomedical engineering
Computational modeling of cardiovascular function has become a critical part of diagnosing, treating and understanding cardiovascular disease. Most strategies involve constructing anatomically accurate computer models of cardiovascular structures, wh...

Physiological control for left ventricular assist devices based on deep reinforcement learning.

Artificial organs
BACKGROUND: The improvement of controllers of left ventricular assist device (LVAD) technology supporting heart failure (HF) patients has enormous impact, given the high prevalence and mortality of HF in the population. The use of reinforcement learn...