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

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.

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.