Unveiling Complexity: Synergizing Deep Learning and Computational Fluid Dynamics for Blood Flow Analysis

Deep learning and healthcare have recently seen major advancements. The study introduces an innovative framework for analyzing and forecasting blood flow behavior in complex anatomical structures using computational fluid dynamics (CFD) simulations and deep learning methods. The synergy enhances accuracy and efficiency in characterizing blood flow patterns, turbulence, and hemodynamic parameters. In the proposed study, a comprehensive dataset of patient-specific vascular geometries and corresponding hemodynamic conditions is employed to train a deep neural network. The neural network is designed to learn the intricate relationships between input geometry, flow conditions, and the resulting blood flow patterns obtained from CFD simulations. By undergoing a series of iterative training stages, the neural network becomes proficient in precisely forecasting blood flow attributes, consequently leading to a substantial reduction in the computational workload typically associated with conventional CFD simulations. Furthermore, the developed framework provides valuable insights into the impact of various physiological factors on blood flow dynamics. The deep learning model’s ability to generalize from the training data allows for the exploration of a wide range of scenarios, contributing to a deeper understanding of the underlying mechanisms governing blood flow behavior. The study shows that the developed neural networks accurately predict blood flow attributes, supported by architecture-specific accuracy scores. Notably, the neural network attains an overall accuracy of 0.9970, outperforming other architectures, such as LSTM (0.9779), GRU (0.9916), and RNN (0.9888). To affirm the efficacy of the CFD-deep learning framework, thorough comparisons are conducted between the model’s predictions and conventional CFD simulations. The results demonstrate a notable convergence between the two methods, validating the accuracy and reliability of deep learning predictions.