A review has shed light on the groundbreaking advancements in the simulation of blood flow within the intricate vascular system that could transform medical treatment and device innovation for vascular diseases.
Modelling vascular flow is crucial for understanding and treating vascular diseases, but traditional methods are labour and computationally intensive. The new research, published in the Journal of the Royal Society Interface, evaluates state-of-the-art methods that accelerate the simulation process while retaining the level of accuracy required for such crucial applications.
The researchers, led by The University of Manchester, found that Reduced Order Modelling (ROM) - a technique for reducing the computational complexity - can be used selectively to accurately accelerate various types of vascular flow modelling problems.
They also found that Machine Learning methods can be used to overcome limitations in ROM techniques or to provide entirely new simulation techniques that can tackle a wide array of vascular flow modelling problems.
The findings have the capacity to revolutionise the vascular medical field.
The review also highlights the significance of these accelerated simulation methods for in-silico trials, which are virtual simulations integral to the development and regulatory approval of new medical devices. Using these simulation acceleration techniques, in-silico trials can be conducted with unprecedented speed and accuracy, reducing reliance on conventional clinical trials that are often expensive and time consuming.
The research also advocates for a concerted effort to establish a benchmarking framework for simulation acceleration methods. This initiative would establish standardised metrics for evaluating precision and speed-up across different simulation approaches, encouraging transparency and comparability in this rapidly advancing field.
