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Details of Grant 

EPSRC Reference: EP/W000091/1
Title: In-Procedure Personalized Atrial Digital Twin to Predict Outcome of Atrial Fibrillation Ablation
Principal Investigator: Niederer, Professor SA
Other Investigators:
O'Neill, Professor M Wilkinson, Professor RD Clayton, Professor RH
Researcher Co-Investigators:
Dr C Corrado
Project Partners:
Biosense Webster Georgia Institute of Technology Medical University of Graz
Polytechnic University of Milan Rochester Institute of Technology The Alan Turing Institute
University of Edinburgh
Department: Imaging & Biomedical Engineering
Organisation: Kings College London
Scheme: Standard Research
Starts: 01 April 2022 Ends: 30 April 2023 Value (£): 1,534,181
EPSRC Research Topic Classifications:
Human-Computer Interactions Statistics & Appl. Probability
Theoretical biology
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
12 Aug 2021 Healthcare Technologies Investigator Led Panel Aug 2021 Announced
Summary on Grant Application Form
Atrial fibrillation (AF) is a disorder of heart rhythm that predominantly affects older people, increasing the risk of stroke and reducing quality of life. AF can be treated by radio-frequency ablation, which irreversibly destroys regions of tissue that initiate and sustain the arrhythmia. Currently, success of the ablation can only be evaluated after the therapy has been delivered.

Cardiac digital twins provide a physics-based representation of the patient that is updated as more information becomes available. These twins can be used to predict how a patient will respond to an AF ablation. We propose to use digital twins of patients undergoing an AF ablation procedure, created from both pre-procedure imaging, and importantly, detailed in-procedure invasive measurements, to predict how the atria will respond to ablation. This will provide the basis for creating digital twins that can be used to guide and optimise AF ablation therapies for individual patients.

Heart models for individual patients have been created by us and others, but the personalisation process is time intensive because the models have many degrees of freedom. The main challenge for this project is therefore to accelerate both personalisation and prediction so that they can be achieved within the 30-45 minute time window that is available during a clinical procedure.

We will address this challenge by (1) optimising the use of information about an individual patient that can be gathered prior to the procedure, as well as information from a population of previous patients; (2) identifying model parameters that are most informative about an individual and which can be identified from clinical measurements; (3) selecting effective methods to accelerate computational simulations; and (4) a systematic investigation of novel approaches for rapid in-procedure personalisation, including state-of-the-art machine learning methods. A probabilistic approach will be taken throughout, and predictions will depend on the quality of the data and model fits. Our workflow will then be integrated to produce an easy to use software platform that is suitable for deployment in the clinical setting for a proof-of-concept clinical study in 20 patients.

The project will develop the technology, generate pilot data, create a business case, and engage with patients so that we can undertake a separate and extensive clinical study to establish the effectiveness of our workflow and its suitability for routine clinical use.

Key Findings
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