Application of Deep Reinforcement Learning to Predict Ablation Therapy for Atrial Fibrillation from Imaging Data

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Aim of the PhD Project:Develop patient image-based models of atrial fibrillation (AF), the most common arrhythmia. Apply the models to simulate multiple scenarios of AF and its termination by ablation therapy. Train deep reinforcement learning algorithms to predict the optimal patient-specific ablation. Validate the predictions using electro-anatomical atrial mapping data from the same patients. Project description:Atrial fibrillation (AF) the most common sustained cardiac arrhythmia that affects about 33 million people worldwide. The disease is associated with increased levels of morbidity and mortality, high risks of developing heart failure and stroke, and therefore very high rates of patient hospitalizations. The overall economic burden of AF amounts to 1% of total healthcare costs in the UK alone. Even advanced first-line therapies, such as catheter ablation (CA), are highly empirical and have poor long-term outcomes, with about half of AF patients returning for the repeated procedures, which further contributes to the healthcare burden. This warrant the development of novel approaches that can improve the efficacy of CA therapy and clinical outcomes in a large patient population. This project will apply deep reinforcement (RL) learning in combination with patient MR imaging (to provide structural information of the atria) and MRI-based modelling (to provide functional information) to design patient-specific CA strategies that will help clinicians and improve treatment success rates. To achieve this, patient-specific 3D left atrial (LA) models will be derived from MRI scans of AF patients and used to simulate patient-specific AF scenarios. Then a RL algorithm will be created, where an ablating agent moves around the 3D LA, applying CA lesions to terminate AF and learning through feedback imposed by a reward policy. The algorithm will be trained to learn from the simulations linked with the underlying patient MRI data and identify optimal CA therapy in each case. After the training and validation, the RL algorithm will predict the optimal CA therapy from the images only, providing a fast, clinically-compatible tool for personalising CA therapy for each patient, and ultimately improving treatment of this common disease in a large patient population. Image-guided procedures are increasingly used to move away from empirical treatments and improve the patient outcomes. However, even advanced imaging systems do not provide crucial functional information about the origins of AF, and the success of image-based patient stratification and CA guidance remains suboptimal. Image-based modelling can provide the missing functional information by predictive simulations of the 3D LA function in a given patient, particularly by linking atrial structural features obtained from MR imaging with AF arrhythmogenesis. Downsides of this approach include (i) substantial computational power needed to simulate multiple AF scenarios in detailed 3D atrial models and (ii) the need to rerun the models each time additional patient data is integrated into them, which both make the application of models in a clinical setting impractical. The application of RL will help overcome such limitations: once the RL algorithm is trained using the linked imaging and image-based modelling data, it will provide a fast tool to identify optimal CA therapy for patients outside of the training cohort based on image only, without the need to run simulations. These predictions will be validated against clinical data available from the patients.