Fast characterization of inducible regions of atrial fibrillation models with multi-fidelity Gaussian process classification

TL;DR

This paper introduces a multi-fidelity Gaussian process classification method on Riemannian manifolds to efficiently identify inducible atrial fibrillation regions, improving accuracy and speed for clinical modeling.

Contribution

It presents a novel probabilistic classifier that combines low and high-resolution atrial models directly on the surface for better arrhythmia inducibility prediction.

Findings

10% higher balanced accuracy than baseline

Seamless integration of multi-resolution models

Potential for faster clinical application

Abstract

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.