[HTML][HTML] Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review
Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital
Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which …
Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which …
Mesh U-Nets for 3D cardiac deformation modeling
During a cardiac cycle, the heart anatomy undergoes a series of complex 3D deformations,
which can be analyzed to diagnose various cardiovascular pathologies including …
which can be analyzed to diagnose various cardiovascular pathologies including …
Multiscale graph convolutional networks for cardiac motion analysis
We propose a multiscale spatio-temporal graph convolutional network (MST-GCN)
approach to learn the left ventricular (LV) motion patterns from cardiac MR image …
approach to learn the left ventricular (LV) motion patterns from cardiac MR image …
Post-infarction risk prediction with mesh classification networks
Post-myocardial infarction (MI) patients are at risk of major adverse cardiac events (MACE),
with risk stratification primarily based on global image-based biomarkers, such as ejection …
with risk stratification primarily based on global image-based biomarkers, such as ejection …
Deep Spatio-Temporal Multiplex Graph Learning for Cardiac Imaging Classification
Cardiovascular diseases are the leading cause of mortality worldwide. Cardiac imaging is
critical for precise characterization of cardiac structure and function, and is key in diagnosis …
critical for precise characterization of cardiac structure and function, and is key in diagnosis …