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

Abstract and Introduction

Abstract

The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep learning techniques has opened new frontiers in electrocardiography analysis including signature identification of diseased states. Artificial intelligence advances coupled with simultaneous rapid growth in computational power, sensor technology, and availability of web-based platforms have seen the rapid growth of AI-aided applications and big data research. Changing lifestyles with an expansion of the concept of internet of things and advancements in telecommunication technology have opened doors to population-based detection of atrial fibrillation in ways, which were previously unimaginable. Artificial intelligence-aided advances in 3D cardiac imaging heralded the concept of virtual hearts and the simulation of cardiac arrhythmias. Robotics, completely non-invasive ablation therapy, and the concept of extended realities show promise to revolutionize the future of EP. In this review, we discuss the impact of AI and recent technological advances in all aspects of arrhythmia care.

Graphical Abstract

Artificial intelligence-enhanced arrhythmia care.

Introduction

As artificial intelligence (AI) has entered the medical field in recent years, machine learning (ML) approaches have made progress in assisting healthcare professionals in optimizing personalized treatment in a given situation, in particular in electrocardiography and image interpretation. Artificial intelligence methodologies are increasingly being adopted into all aspects of patient care and are paving the way to minimally invasive or non-invasive treatment modalities. This article offers a state-of-the-art overview on milestones achieved, but also on future integration of this information into diagnostic and therapeutic measures, and its likely impact on all aspects of arrhythmia care. Integration of all individual information in combination with AI solutions is likely to revolutionize electrophysiology (EP) interventions in the near future (Figure 1 and Graphical Abstract).

Figure 1.

An illustration highlighting the impact of artificial intelligence and recent technological advancements on all aspects of patient care in the field of cardiac electrophysiology. ADAS, Automatic Detection of Arrhythmic Substrate; AF, atrial fibrillation; BSM, body surface mapping; CIE, computerized interpretation of electrocardiography; DL, deep learning; EAM, electro anatomical mapping; EP, electrophysiology; LGE, late gadolinium enhancement; MDCT, multidetector computed tomography; ML, machine learning; MRI, magnetic resonance imaging; OPTIMA, optimal target identification via modelling of arrhythmogenesis; SBRT, stereotactic body radiotherapy; SR, sinus rhythm; TA, texture analysis; TC, tissue characterization; VAAT, virtual heart arrhythmia ablation targeting; VARP, virtual heart arrhythmia risk predictor approach; VIVO, view into ventricular onset; VT, ventricular tachycardia.

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