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

Disclosures

Eur Heart J. 2021;42(38):3904-3916. 

In This Article

Robotics in Electrophysiology and Potential Role of Machine Learning

To deliver catheter ablation safely to a high degree of precision reducing the impact of operator variability in level of training and technical skill, robotic ablation can be seen as a valuable tool to help improve access to the same level of accuracy in a reproducible fashion.

In the field of EP, robotic navigation was introduced 20 years ago. The two concepts proposed were either a mechanical sheath system guiding a conventional ablation catheter via computer-enhanced technique (Hansen & Amigo)[88–90] or a magnetic platform (Stereotaxis).[89,91]

A more recent contender is another mechanical system, which uses acoustic energy for both imaging and lesion deployment (Vytronus). 3D reconstruction using ultrasound imaging is performed automatically and the robotic system deploys acoustic energy completely automatically along an operator designed ablation line. First-in-human experience was reported in a cohort of 52 patients with paroxysmal AF undergoing pulmonary vein isolation using low intensity collimated ultrasound (LICU). Acute pulmonary vein isolation was achieved in 77.3% and 94.2% of patients using LICU only and LICU with enhanced software, respectively, with continued freedom from atrial arrhythmia recurrence at 12 months.[92]

A vast body of evidence has been published for the magnetic navigation system (Niobe, Stereotaxis), which in combination with 3D EAM systems (CARTO or ACUTUS) plus 3D image integration, can be applied to all arrhythmias.[93,94]

Feasibility of using an ML algorithm to guide automated electro-anatomical voltage mapping was previously demonstrated using remote magnetic navigation system. The ML algorithm used learning from demonstration framework utilizing prior knowledge from expert mapping procedures and Gaussian process model-based active learning.[95]

Non-invasive ML-aided identification of the ablation targets using 3D imaging and personalized heart modelling followed by robotic ablation of these pre-defined locations appears to be an exciting prospect for the future. This approach, if successful, could limit the number of catheters to a minimum and could be both more time and cost-efficient.

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