Modeling Transient Cardiovascular Hemodynamics With Physiological Conscious Autoencoder

Current biomedical research relies primarily on in silico studies to model complex systems like cardiovascular hemodynamics. However, for comprehensive validation of mathematical models, real in vitro experiments are indispensable. This paper introduces a framework that bridges these approaches through the hybrid mock circulatory loop (hMCL), offering precise control, flexibility, and reproducibility. This innovation enables the investigation of cardiovascular disease mechanisms in a controlled setting, overcoming the limitations of live organism studies. The framework employs a modified autoencoder with a partially guided latent space, incorporating physiological insights into a deep neural network. It leverages time-delayed cardiovascular signals, including pressures, flow rates, and unmeasurable cardiovascular system (CVS) parameters, to estimate critical parameters like aortic and mitral resistance, systemic resistance, and left ventricle elastance. The autoencoder’s loss function is tailored to predict these parameters, enhancing the understanding of cardiovascular dynamics. The study utilizes in silico data to train the model and validates it through in vitro tests using a hybrid mock loop device, yielding a remarkable accuracy of less than 5.7% error in replicating CVS signals. Furthermore, the framework demonstrates the adaptability of CVS variables to perturbations in closed-loop conditions and exhibits a diagnostic model with an impressive 98.55% F1 score for classifying cardiovascular disease severity. This research significantly advances the field by modifying the autoencoder to include physiological signals, introducing a novel loss function, developing a structured network, presenting a diagnostic model, and proposing an innovative approach for generating transient responses in cardiovascular hemodynamics, validated through in vitro and in silico experiments.