Mapping populations to patients: designing optimal ablation therapy for atrial fibrillation through simulation and deep learning of digital twin

We will combine biophysical simulation and deep learning methods with a longitudinal digital twin approach to optimise risk prediction and choice of therapy for atrial fibrillation. We aim to move predictions from the acute response to the long-term response; from the average patient to an individual patient; from standard treatments to any treatment approach; from small patient cohorts to large virtual trials; and from long simulation times to short clinical timescales.

Development and Validation of Smartphone-Based Tools for Characterisation of Gait

Virtual Atria with Personalised Electrophysiology for Atrial Fibrillation Therapy Planning - SEMS Industry-supported PhD Studentship

Predicting Atrial Fibrillation Mechanisms Through Deep Learning

Medical Research Council Skills Development Fellowship. The objective is to develop a combined biophysical simulation and deep learning network pipeline that accurately quantifies the likelihood of success of pulmonary vein isolation for an individual patient to guide ablation therapy.