Integrating deep learning with computational fluid dynamics can accelerate patient-specific hemodynamics simulations and may lead to novel non-invasive computational medical decisions.
Deep learning approaches have the potential to significantly accelerate computationally expensive patient-specific hemodynamics simulations, facilitating their transition to clinical tools.
Computational fluid dynamics (CFD) modeling of blood flow plays an important role in better understanding various medical conditions, designing more effective drug delivery systems, and developing novel diagnostic methods and treatments. However, despite significant advances in computational technology and resources, the expensive computational cost of these simulations still hinders their transformation from a research interest to a clinical tool. This bottleneck is even more severe for image-based, patient-specific CFD simulations with realistic boundary conditions and complex computational domains, which make such simulations excessively expensive. To address this issue, deep learning approaches have been recently explored to accelerate computational hemodynamics simulations. In this study, we review recent efforts to integrate deep learning with CFD and discuss the applications of this approach in solving hemodynamics problems, such as blood flow behavior in aorta and cerebral arteries. We also discuss potential future directions in the field. In this review, we suggest that incorporating physiologic understandings and underlying fluid mechanics laws in deep learning models will soon lead to a paradigm shift in the development novel non-invasive computational medical decisions.
Amirtahà Taebi (Thu,) conducted a review in Computational hemodynamics. Deep learning approaches was evaluated. Integrating deep learning with computational fluid dynamics can accelerate patient-specific hemodynamics simulations and may lead to novel non-invasive computational medical decisions.