Trayanova, Natalia A

Abstract Aims Artificial intelligence (AI) has the potential to transform cardiac electrophysiology (EP), particularly in arrhythmia detection, procedural optimization, and patient outcome prediction. However, a standardized approach to reporting and understanding AI-related research in EP is lacking. This scientific statement aims to develop and apply a checklist for AI-related research reporting in EP to enhance transparency, reproducibility, and understandability in the field. Methods and results An AI checklist specific to EP was developed with expert input from the writing group and voted on using a modified Delphi process, leading to the development of a 29-item checklist. The checklist was subsequently applied to assess reporting practices to identify areas where improvements could be made and provide an overview of the state of the art in AI-related EP research in three domains from May 2021 until May 2024: atrial fibrillation (AF) management, sudden cardiac death (SCD), and EP lab applications. The EHRA AI checklist was applied to 31 studies in AF management, 18 studies in SCD, and 6 studies in EP lab applications. Results differed between the different domains, but in no domain reporting of a specific item exceeded 55% of included papers. Key areas such as trial registration, participant details, data handling, and training performance were underreported (<20%). The checklist application highlighted areas where reporting practices could be improved to promote clearer, more comprehensive AI research in EP. Conclusion The EHRA AI checklist provides a structured framework for reporting AI research in EP. Its use can improve understanding but also enhance the reproducibility and transparency of AI studies, fostering more robust and reliable integration of AI into clinical EP practice.

Despite the global COVID-19 pandemic, during the past 2 years, there have been numerous advances in our understanding of arrhythmia mechanisms and diagnosis and in new therapies. We increased our understanding of risk factors and mechanisms of atrial arrhythmias, the prediction of atrial arrhythmias, response to treatment, and outcomes using machine learning and artificial intelligence. There have been new technologies and techniques for atrial fibrillation ablation, including pulsed field ablation. There have been new randomized trials in atrial fibrillation ablation, giving insight about rhythm control, and long-term outcomes. There have been advances in our understanding of treatment of inherited disorders such as catecholaminergic polymorphic ventricular tachycardia. We have gained new insights into the recurrence of ventricular arrhythmias in the setting of various conditions such as myocarditis and inherited cardiomyopathic disorders. Novel computational approaches may help predict occurrence of ventricular arrhythmias and localize arrhythmias to guide ablation. There are further advances in our understanding of noninvasive radiotherapy. We have increased our understanding of the role of His bundle pacing and left bundle branch area pacing to maintain synchronous ventricular activation. There have also been significant advances in the defibrillators, cardiac resynchronization therapy, remote monitoring, and infection prevention. There have been advances in our understanding of the pathways and mechanisms involved in atrial and ventricular arrhythmogenesis.

The National Heart, Lung, and Blood Institute (NHLBI) convened a workshop of cardiologists, cardiac electrophysiologists, cell biophysicists, and computational modelers on August 20 and 21, 2007, in Washington, DC, to advise the NHLBI on new research directions needed to develop integrative approaches to elucidate human cardiac function. The workshop strove to identify limitations in the use of data from nonhuman animal species for elucidation of human electromechanical function/activity and to identify what specific information on ion channel kinetics, calcium handling, and dynamic changes in the intracellular/extracellular milieu is needed from human cardiac tissues to develop more robust computational models of human cardiac electromechanical activity. This article summarizes the workshop discussions and recommendations on the following topics: (1) limitations of animal models and differences from human electrophysiology, (2) modeling ion channel structure/function in the context of whole-cell electrophysiology, (3) excitation–contraction coupling and regulatory pathways, (4) whole-heart simulations of human electromechanical activity, and (5) what human data are currently needed and how to obtain them. The recommendations can be found on the NHLBI Web site at http://www.nhlbi.nih.gov/meetings/workshops/electro.htm.