Richard Wilkinson: Uncertainty quantification for heart surgeons
Channel: NorthWestBiomathsDataScienceSeminars
Date: 2020-12-14

North West Seminar Series of Mathematical Biology and Data Science 

Monday, 30th November 2020 (hosted by Carl Whitfield) 

https://www.cms.livjm.ac.uk/APMSeminar/

Abstract:
Atrial fibrillation (AF) is a type of cardiac arrhythmia that affects about 1 million people in the UK, and is caused when electrical waves in the atria become disorganised, reducing the ability of the heart to pump blood. Catheter ablation is used to remove abnormal tissue, with the aim of stopping the electrical pathways that sustain the arrhythmia. Surgeons must decide which tissue to ‘ablate’ on the basis of limited noisy data: MRI scans of the heart done in advance of the surgery, and the results of electrophysiology studies performed during surgery. About 40% of patients treated for AF go on to suffer atrial tachycardia (another type of arrhythmia) which may then require further surgery (with associated risks and costs).

In this talk, I will discuss ongoing work that aims to combine the available data on a patient with complex simulation models of electrical activation in the atria, using experience learnt from operations on previous patients, to build a personalised digital twin that can be used during a time-pressured surgery to guide the ablation process.