A Novel LSTM and Graph Neural Networks Approach for Cardiovascular Simulations

We propose a novel method that integrates Long Short-Term Memory (LSTM) networks with Graph Neural Networks (GNNs) to build reduced-order models of cardiovascular simulations. Reduced-order models are often used as an alternative to full three-dimensional cardiovascular simulations, providing a way to simplify the computational demands associated with fully detailed 3D simulations. The proposed method encodes blood fluid dynamics within a MeshGraphNet-based framework, which is particularly effective in modeling complex physical systems by leveraging graph structures to represent the state of the system. Our method extends the capabilities of the original framework by incorporating LSTMs to capture long-term dependencies, thereby improving predictive accuracy and significantly reducing the computational resources required for the training process. This method achieves errors below 2% for blood pressure and flow rate predictions, showcasing a 65% improvement in average error rates compared to the MeshGraphNet-based framework and a notable increase in computational efficiency, reducing training time by at least 57%. Our method also introduces the ability to adapt the simulation to different cardiac cycles depending on the patient, providing a robust and efficient tool for patient-specific cardiovascular modeling.