  Computational fluid dynamics (CFD) is a valuable tool for personalised,
non-invasive evaluation of hemodynamics in arteries, but its complexity and
time-consuming nature prohibit large-scale use in practice. Recently, the use
of deep learning for rapid estimation of CFD parameters like wall shear stress
(WSS) on surface meshes has been investigated. However, existing approaches
typically depend on a hand-crafted re-parametrisation of the surface mesh to
match convolutional neural network architectures. In this work, we propose to
instead use mesh convolutional neural networks that directly operate on the
same finite-element surface mesh as used in CFD. We train and evaluate our
method on two datasets of synthetic coronary artery models with and without
bifurcation, using a ground truth obtained from CFD simulation. We show that
our flexible deep learning model can accurately predict 3D WSS vectors on this
surface mesh. Our method processes new meshes in less than 5 [s], consistently
achieves a normalised mean absolute error of $\leq$ 1.6 [%], and peaks at 90.5
[%] median approximation accuracy over the held-out test set, comparing
favourably to previously published work. This demonstrates the feasibility of
CFD surrogate modelling using mesh convolutional neural networks for
hemodynamic parameter estimation in artery models.
