Hemodynamic Analysis

Hemodynamic analysis focuses on understanding and quantifying blood flow dynamics within the cardiovascular system, primarily aiming to improve diagnosis, prognosis, and treatment of cardiovascular diseases. Current research heavily utilizes machine learning, particularly deep learning architectures like graph neural networks, variational autoencoders, and transformer networks, to create efficient and accurate predictive models of hemodynamics from various imaging modalities (e.g., MRI, CT, ultrasound) and physiological signals (e.g., ECG). These advancements offer the potential for faster, less invasive, and more personalized approaches to cardiovascular care, enabling earlier diagnosis and improved treatment planning.