Predicting atrial fbrillation treatment outcomes through mathematical modelling, signal processing and machine learning
Catheter ablation and antiarrhythmic drug therapy approaches for treatment of atrial fibrillation are sub-optimal. This is in part because it is challenging to predict long-term response to therapy from short-term measurements, which makes it difficult to select optimal patient-specific treatment approaches. Clinical trials identify patient demographics that provide prediction of long-term response to standard treatments across populations. Patient-specific biophysical models can be used to assess novel treatment approaches but are typically applied in small cohorts to investigate the acute response to therapies. Our overall aim is to use machine learning approaches together with patient-specific biophysical simulations to predict long-term atrial fibrillation recurrence after ablation or drug therapy in large populations.

In this talk I will present our methodology for constructing personalised atrial models from patient imaging and electrical data; present results from biophysical simulations of ablation treatment; and finally explain how we are combining these methodologies with machine learning techniques for predicting long-term treatment outcomes.
Date: 19 November 2021, 14:00 (Friday, 6th week, Michaelmas 2021)
Venue: Mathematical Institute, Woodstock Road OX2 6GG
Venue Details: L3
Speaker: Dr Caroline Roney (Kings’ College London)
Organising department: Mathematical Institute
Organiser: Sara Jolliffe (University of Oxford)
Organiser contact email address: sara.jolliffe@maths.ox.ac.uk
Host: Dr Peter Minary (University of Oxford)
Part of: Mathematical Biology and Ecology
Booking required?: Not required
Audience: Members of the University only
Editor: Sara Jolliffe