  Computational fluid dynamics (CFD) can be used to simulate vascular
haemodynamics and analyse potential treatment options. CFD has shown to be
beneficial in improving patient outcomes. However, the implementation of CFD
for routine clinical use is yet to be realised. Barriers for CFD include high
computational resources, specialist experience needed for designing simulation
set-ups, and long processing times. The aim of this study was to explore the
use of machine learning (ML) to replicate conventional aortic CFD with
automatic and fast regression models. Data used to train/test the model
consisted of 3,000 CFD simulations performed on synthetically generated 3D
aortic shapes. These subjects were generated from a statistical shape model
(SSM) built on real patient-specific aortas (N=67). Inference performed on 200
test shapes resulted in average errors of 6.01% +/-3.12 SD and 3.99% +/-0.93 SD
for pressure and velocity, respectively. Our ML-based models performed CFD in
+/-0.075 seconds (4,000x faster than the solver). This proof-of-concept study
shows that results from conventional vascular CFD can be reproduced using ML at
a much faster rate, in an automatic process, and with high accuracy.
