Abstract:

While atrial fibrillation (AF) is currently the most common arrhythmia worldwide, finding theoptimal treatment for each patient remains a challenge. One option for treating AF patientsis pulmonary vein isolation (PVI). PVI is an established rhythm control strategy. However,PVI often fails for some patients, leading to high recurrence rates, especially for those withpersistent AF. To treat these patients in whom PVI failed, ablation outside the pulmonaryveins can be performed. However, finding the right spots for ablation is demanding and time-consuming. To better identify suitable regions for ablation, electrophysiological simulationscan be used to assess AF vulnerability before the ablation procedure. These simulationscurrently require substantial computational power, making them unfeasible for smallerinstitutions or hospitals. Therefore, this thesis uses machine learning (ML) to assess andaccelerate the vulnerability assessment of AF patient-specific models.The ML pipeline consists of a feature-based approach where global clinical featuresof the atrium were calculated based on late gadolinium enhancement magnetic resonanceimaging (LGE-MRI). In addition, local features around single stimulation points werecalculated. Data from 22 patients with AF were used to create personalized patient models.AF vulnerability of these 22 patients was then calculated using the PEERP pacing protocolwith the electrophysiological simulator openCARP.A random forest (RF) classifier was used with the previously calculated global and localfeatures to predict the results from the pacing protocol for inducibility. This algorithm wasfurther improved using hyperparameter tuning and different optimization strategies. Thetuned algorithm achieved a mean area under the curve (AUC) receiver operating charac-teristic (ROC) of 0.75 ± 0.03 in predicting point inducibility with 10-fold cross-validation.Discarding 50% of the test samples based on RF certainty analysis achieved a false negativerate of 5%. Using this classifier configuration, up to 10% of all stimulation points areexcluded, resulting in a 10% reduction in simulation time.To better assess the influence of different features on the RF classifier, we used SHAPto get an insight into feature importance. The analysis showed that especially local fibrosisdensity around stimulation points had the highest impact on the prediction (SHAP valuesfrom −0.15 to 0.1 ). Additionally, SHAP showed that the inducibility for a stimulation pointdecreases if it is moved to the atrial appendages.This work not only provides an approach to saving 10% of simulation time for assessingatrial fibrillation vulnerability but also uses SHAP to explain and assess predictions of theunderlying random forest classifier.