Simulation-free prediction of atrial fibrillation inducibility with the fibrotic kernel signature

Computational models of atrial fibrillation (AF) can help improve success rates of interventions, such as ablation. However, evaluating the efficacy of different treatments requires performing multiple costly simulations by pacing at different points and checking whether AF has been induced or not, hindering the clinical application of these models. In this work, we propose a classification method that can predict AF inducibility in patient-specific cardiac models without running additional simulations. Our methodology does not require re-training when changing atrial anatomy or fibrotic patterns. To achieve this, we develop a set of features given by a variant of the heat kernel signature that incorporates fibrotic pattern information and fiber orientations: the fibrotic kernel signature (FKS). The FKS is faster to compute than a single AF simulation, and when paired with machine learning classifiers, it can predict AF inducibility in the entire domain. To learn the relationship between the FKS and AF inducibility, we performed 2371 AF simulations comprising 6 different anatomies and various fibrotic patterns, which we split into training and a testing set. We obtain a median F1 score of 85.2% in test set and we can predict the overall inducibility with a mean absolute error of 2.76 percent points, which is lower than alternative methods. We think our method can significantly speed-up the calculations of AF inducibility, which is crucial to optimize therapies for AF within clinical timelines. An example of the FKS for an open source model is provided in https://github.com/tbanduc/FKS_AtrialModel_Ferrer.git.