Predicting Atrial Fibrillation Mechanisms Through Deep Learning

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Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting over 1.1 million people in the UK alone, and is associated with increased risk of other cardiovascular disease, stroke and death. Patients who do not respond to drug treatment may be treated using radio frequency catheter ablation therapy, which is used to isolate the areas of pathological tissue responsible for AF. AF patients require different amounts of treatment: some patients require multiple procedures, with more extensive ablation strategies; while for others, a more simple isolation of the pulmonary veins using radio frequency catheter ablation is sufficient. Predicting whether an ablation treatment approach is a sufficient treatment for a particular patient is a clinical challenge, which if solved could improve safety of ablation procedures, and decrease time and cost for these procedures.Computational biophysical simulations personalised to patient properties, including cardiac imaging and electrical data, may offer substantial insights into AF and how to treat it, but run too slowly to be used during clinical procedures. My objective is to develop a combined biophysical simulation and machine-learning network pipeline that accurately quantifies the likelihood of success of ablation therapies for an individual patient quickly enough for use during a clinical procedure, to guide ablation therapy. The machine-learning network will be trained to large quantities of biophysical simulated data to ensure that it correctly captures the physics and physiology of the system. The training will then be augmented with the complexity and reality of clinical data. Finally, the deep learning pipeline will be tested in a retrospective study.We hope that this study will provide a proof of concept for this predictive pipeline. Our novel approach has the potential to revolutionise the field of predictive modelling for AF by constructing a pipeline that enables patient-specific treatment approaches to be developed and applied during a single ablation procedure. We hope that in the future, clinical and research centres will be able to use the trained machine learning network to predict the factors responsible for AF in an individual patient and the outcome of different ablation procedures. This may lead to improved safety of ablation procedures, better patient selection, as well as decreased time and cost for these procedures.