Aortic disease is a leading killer worldwide, and despite perceived progress in diagnostic and therapeutic techniques, the overall global death rate from aortic disease is growing. Its diagnosis is time sensitive and depends on cross-sectional imaging. Additionally, up to 50% of aortic dissection patients are initially misdiagnosed as having other conditions.
Machine learning techniques have been used to successfully increase accuracy and reduce the time taken to diagnose the condition. However, conventional modelling methods are usually computationally exhaustive, time consuming and not able to produce predictions in real time, which are essential for diagnostic purposes.
Supported by OCRE cloud funding, a research project at University of Galway aims to utilise cloud solutions from AWS (provided through Rackspace) to introduce new methods that will significantly reduce the complexity and time of the diagnosis, bringing real time predictions within reach and broadening research horizons.
Read the full success story on the OCRE website: https://www.ocre-project.eu/success-story/deep-learning-methods-tackling-aortic-disease-progression
