Atrial Fibrillation (AF) is the most common cardiac arrhythmia, with incidence increasing with
age where 10% of the population above 80 years is afflicted with it. As AF is progressive, over time it is harder
to treat, which increased risk of stroke, dementia and heart failure. The most effective treatment
is catheter ablation therapy (CAT) which selectively destroys tissue to create lesions blocking
conduction; however, CAT follows generic patterns, without personalisation. AF often recurs after
treatment, with more than 25% of patients requiring re-ablation after 2 years.
We aim to develop a personalised medicine approach based on computer modelling, to plan AF
ablation to prevent recurrence. We propose to use physiological digital twins of patient hearts,
created from imaging (MRI/CT), and calibrated using machine learning, to analyse and fit ECG and
electrogram recordings acquired clinically, from implantable devices or wearables. Novel
technology for real-time simulation of AF will be developed and integrated in a clinically viable
platform to support the easy flow, robust analysis and interpretation of information, to achieve a
scalable translation to large cohorts, and, thus, to enable clinicians to speed up the translation of
observations to diagnosis and therapy planning.
Due to inherent uncertainty in measurements, anatomical structures, and properties, multiple AF
scenarios will be simulated to derive biomarkers for assessing risk of AF progression, and
determine potential ablation sets for each individual prior to CAT. Intraoperatively,
electroanatomic recordings will be used on-the-fly to determine which simulation corresponds
best to the patient, with its optimal ablation set. Platform development will use large-scale
retrospective clinical data, but will be equally applicable to prospective trials. Economic analysis
will evaluate benefits arising from early preventative and longer-lasting treatment, reduced
duration and procedural risks of interventions.
Dr. Edward Vigmond presents the DAWN-AF program at the EP PerMed Conference, Berlin, 2025. © Detlef Eden
EP PerMed Conference on Personalised Medicine Research, Berlin, Feb. 11 - 12, 2025. © Detlef Eden
Preliminary results
A (top) Real-time simulation of ventricular tachycardia entrainment and
associated ECG and electrograms; (bottom) co-registration of anatomical model with clinical EAM data.
B
(top) Atrial EP modelling work on UACs
[1], (middle) detailed representation of anatomical structures, and
(bottom) simulations of different AF patterns
[2].
C Model calibration techniques for estimating activation
sites, fibre architecture and conduction velocities from sparse activation maps for ventricles (top row) and
atria (bottom rows)
[3]. Abbreviations: Electrocardiogram (ECG), Electro-anatomical Mapping (EAM),
Border Zone (BZ), Right Ventricle (RV), Left Ventricle (LV), Electrogram (EGM), Electrophysiology (EP),
Universal Atrial Coordinates (UACs), Right Atrial Appendage (RAA), Superior Vena Cava (SVC), Bachmann‘s
Bundle (BB), Crista Terminalis (CT), Fossa Ovalis (FO), Sino-atrial Node (SAN), Pectinate Muscles (PM),
Acetylcholine (ACh), Fast Iterative Method (FIM), FIM-Inverse (FIMIN), Physics-Informed Neural Network
(PINN), Activation Time (AT), Local Activation Time (LAT), PIEMAP
[4].
iHEALTH conducts workshop on cardiac digital twins
On March 14, 2025, the "Cardiac Digital Twins" workshop was held at the UC School of Engineering, located on the San Joaquín Campus of the Pontifical Catholic University of Chile. The workshop was organized by Dr. Francisco Sahli, a UC Engineering professor and principal investigator of iHEALTH. During the event, attendees learned about the latest advances in the field from leading international researchers. The opening talk was given by Dr. Edward Vigmond, who presented his work entitled "Functional and Anatomical Variation in Cardiac Modeling."
Cardiac digital twins are advanced computational models that allow for personalized simulations of the heart's behavior. These models combine anatomical and functional data obtained from medical images and electrophysiological recordings, with the goal of predicting the progression of heart disease and optimizing treatments. During the workshop, various approaches to improving the accuracy and efficiency of these models were discussed, including new electrophysiological modeling techniques, the use of physics-informed neural networks (PINNs), and advances in cardiovascular parameter estimation.
This was followed by a series of presentations by young researchers: Dr. Elena Zappon, a postdoctoral researcher at the Computational Cardiology Laboratory at the Medical University of Graz, presented "An efficient end-to-end framework for generating cardiac digital twin electrophysiology." Next, iHEALTH predoctoral researcher Tomás Banduc presented "A fast solver for the complex eikonal equation," followed by a presentation by UC PhD student Efraín Magaña, entitled "Δ-PoIssoNN: learning atrial activation map from P-wave with physics-informed neural networks." Later, Jeremías Garay, a postdoctoral researcher at UC and iHEALTH, presented "Physics-informed neural networks for parameter estimation in blood flow models."
After a coffee break, the workshop continued with a talk by Dr. René Botnar, director of the Institute for Biological and Medical Engineering at UC and principal investigator of iHEALTH, entitled "Latest advances in cardiovascular MR imaging." Finally, the event concluded with a presentation by Dr. Gernot Plank, professor of computational cardiology at the Medical University of Graz, who spoke about "Computational Model of Cardiac Function - Closing the Gap between Virtual and Physical Reality."
Following the workshop, DAWN-AF researchers Dr. Edward Vigmond, Dr. Gernot Plank, and Dr. Elena Zappon visited the 0.55T MRI scanner located at the iHEALTH Central Hub, accompanied by Dr. René Botnar. This opportunity allowed them to share knowledge about the capabilities of this technology and explore future collaborations in the field of cardiac digital twins.
From left to right: Dr. Edward Vigmond, Dr. René Botnar, Dr. Gernot Plank, Dr. Elena Zappon and Dr. Francisco Sahli.
PUBLICATIONS
Simulation-free prediction of atrial fibrillation inducibility with the fibrotic kernel signature.
Banduc, T., Azzolin, L., Manninger, M., Scherr, D., Plank, G., Pezzuto, S., & Sahli Costabal, F.
Medical image analysis, 99, 103375. Advance online publication. (2024)
pyCEPS: A cross-platform electroanatomic mapping data to computational model conversion platform for the calibration of digital twin models of cardiac electrophysiology.
Arnold, R., Prassl, A. J., Neic, A., Thaler, F., Augustin, C. M., Gsell, M. A. F., Gillette, K., Manninger, M., Scherr, D., & Plank, G.
Computer methods and programs in biomedicine, 254, 108299. (2024)
ForCEPSS-A framework for cardiac electrophysiology simulations standardization.
Gsell, M. A. F., Neic, A., Bishop, M. J., Gillette, K., Prassl, A. J., Augustin, C. M., … Plank, G.
Computer Methods and Programs in Biomedicine, 251(108189), 108189. (2024)
Digital twins for cardiac electrophysiology: state of the art and future challenges.
MJM, Plank G, Heijman J. D
Herzschrittmacherther Elektrophysiol 35(2):118-123. (2024)
Contact
Heart Rythm Disease Institute - IHU Liryc
Hôpital Xavier Arnozan
Avenue du Haut Lévêque
33604 Pessac - France
