Artificial Intelligence in the Diagnosis and Management of Arrhythmias

Venkat D. Nagarajan; Su-Lin Lee; Jan-Lukas Robertus; Christoph A. Nienaber; Natalia A. Trayanova; Sabine Ernst


Eur Heart J. 2021;42(38):3904-3916. 

In This Article

Personalized Virtual Heart Modelling—a New Paradigm

Considerable advances in cellular modelling technology have paved the way for the development of a computerized human cardiac myocyte.[68] Virtual ventricular myocytes, with predefined electrophysiological properties, and predictable functional changes in response to surroundings including ion channel changes and myocardial ischaemia have been developed.[69–74]

Trayanova et al. envisaged the creation of a computerized but personalized virtual heart model.[69,75–77] Contrast-enhanced MR images of the patient's heart were used to create near identical geometrical models of the cardiac chambers and were populated with virtual cardiac myocytes with physiological properties pertaining to the cells from a designated location. Previously validated rule-based algorithms were applied for fibre orientation to compute heart models. These computed virtual hearts were electrophysiological twins to the patient's heart on which various stimulation protocols could be applied to induce ventricular arrhythmias of different morphologies and identify critical isthmus zones for these arrhythmias.

As a proof of concept, this study of virtual heart modelling was used for non-invasive risk assessment of sudden cardiac death in a high-risk population undergoing cardioverter defibrillator implantation. The virtual heart arrhythmia risk predictor approach (VARP) was evaluated retrospectively in a cohort of 41 patients executing simulations to evaluate patient specific VT inducibility and found to be superior to other predictors including left ventricular ejection fraction.[78] Predictive capability of this novel targeted approach needs further evaluation in larger studies.

Yet another proof of concept study evaluated the use of virtual heart modelling in patients undergoing VT ablation.[77] Virtual hearts were modelled from patients' contrast-enhanced MR images and VT induction carried out as in the VARP study. Once VT induction was carried out, the optimal ablation strategy was performed virtually. This technique was termed virtual heart arrhythmia ablation targeting (VAAT). When compared retrospectively in 21 patients undergoing VT ablation, it was found to correspond well with real ablation lesions. The VAAT strategy was further tested prospectively in 5 patients undergoing VT ablation in two different centres. VAAT lesions were merged with an EAM system and an ablation was carried out at these sites without further prior mapping. The clinical outcomes for these patients were encouraging with no further VT episodes post-ablation.[79]