  In several problems involving fluid flows, Computational Fluid Dynamics (CFD)
provides detailed quantitative information, and often allows the designer to
successfully optimize the system, by minimizing a cost function. Sometimes,
however, one cannot improve the system with CFD alone, because a suitable cost
function is not readily available: one notable example is diagnosis in
medicine. The field of interest considered here is rhinology: a correct air
flow is key for the functioning of the human nose, yet the notion of a
functionally normal nose is not available, and a cost function cannot be
written. An alternative and attractive pathway to diagnosis and surgery
planning is offered by data-driven methods. In this work, we consider the
machine-learning study of nasal pathologies caused by anatomic malformations,
with the aim of understanding whether fluid dynamic features, available after a
CFD analysis, are more effective than purely geometric features in the training
of a neural network for regression. Our experiments are carried out on an
extremely simplified anatomic model and a correspondingly simple CFD approach;
nevertheless, they demonstrate that flow-based features perform better than
geometry-based ones, and allow the training of a neural network with fewer
inputs, a crucial advantage in fields like medicine.
