  Computational models of atrial fibrillation have successfully been used to
predict optimal ablation sites. A critical step to assess the effect of an
ablation pattern is to pace the model from different, potentially random,
locations to determine whether arrhythmias can be induced in the atria. In this
work, we propose to use multi-fidelity Gaussian process classification on
Riemannian manifolds to efficiently determine the regions in the atria where
arrhythmias are inducible. We build a probabilistic classifier that operates
directly on the atrial surface. We take advantage of lower resolution models to
explore the atrial surface and combine seamlessly with high-resolution models
to identify regions of inducibility. When trained with 40 samples, our
multi-fidelity classifier shows a balanced accuracy that is 10% higher than a
nearest neighbor classifier used as a baseline atrial fibrillation model, and
9% higher in presence of atrial fibrillation with ablations. We hope that this
new technique will allow faster and more precise clinical applications of
computational models for atrial fibrillation.
