Aortic disease is a leading killer worldwide, and despite perceived progress in diagnostic and therapeutic techniques the burden from aortic disease is growing.
Machine learning techniques have been used to successfully increase accuracy and reduce the time taken to diagnose the condition. In those requiring endovascular intervention for complicated aortic aneurysm, the speed and accuracy of measurement becomes all the more pertinent.
The aim of this project is to model the blood flow in the aorta which has been conventionally done using the finite element models. However, these methods can be computationally exhaustive and time consuming and may not be able to produce predictions in real time essential for diagnostic purposes. Thanks to the OCRE cloud vouchers, this type of real time diagnosis is now within reach and research avenues become significantly broader than previously.
Read the full success story on the OCRE website: https://www.ocre-project.eu/success-story/deep-learning-methods-tackling-aortic-disease-progression
