Development of a Deep Learning Method to Predict Optimal Ablation Patterns for Atrial Fibrillation

Atrial fibrillation (AF) is a common cardiac arrhythmia that affects 1% of the population and is associated with high levels of morbidity and all-cause mortality. Catheter ablation (CA) has become one of the first line treatments for AF, but the success rates of CA and other clinical treatments remain suboptimal. The need to improve clinical outcomes warrants the optimisation of CA therapy. In this study, we develop a novel deep learning method to identify specific ablation patterns that terminate AF efficiently. To achieve this, we simulate typical AF ablation scenarios using computational models of 2D atrial tissue, and use the simulation outcomes as inputs for a deep neural network. The network is trained, validated and then applied to classify the scenarios and predict the optimal CA pattern in each scenario. For the validation dataset, the overall accuracy in identifying the best CA strategy is recorded at 79%. The study provides proof of concept that deep neural networks can learn from computational models of AF and help optimise CA therapy.