Artificial Intelligence in Cardiac Electrophysiology

A special issue of Journal of Cardiovascular Development and Disease (ISSN 2308-3425). This special issue belongs to the section "Electrophysiology and Cardiovascular Physiology".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 1187

Special Issue Editor


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Guest Editor
Division of Cardiology, University of North Carolina, Chapel Hill, NC 27599, USA
Interests: artificial intelligence; machine learning; cardiac electrophysiology; predictive modeling; personalized medicine; signal analysis; big data; supervised learning; unsupervised learning
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Special Issue Information

Dear Colleagues,

Through advanced data analytics and predictive modeling, artificial intelligence (AI) is impacting discovery and healthcare delivery across multiple medical disciplines at an exponential rate. In the field of cardiac electrophysiology, AI applications have improved the accuracy and efficiency of arrhythmia detection and treatment, enhanced risk and outcome prediction, identified novel phenotypic clustering and mechanistic insights, and are poised to reshape diagnostic and therapeutic paradigms. Yet, the field is in relative infancy, with several technological innovations incorporating novel machine learning algorithms, advanced analytics, integrating complex imaging, electrocardiographic and mapping data, and access to large and unique datasets, rapidly advancing frontiers.

This Special Issue will focus on the impact of AI in the field of cardiac electrophysiology, providing an overview of current advancements, practical applications, and future directions. Articles will cover machine learning approaches to solving for arrhythmia and disease phenotypes, AI-driven predictive modelling, focusing on patient outcomes, the implementation of AI both in the electrophysiology lab and broader clinical practice, and will explore ongoing AI research that may shape the future of electrophysiology practice.

We invite contributions broadly encompassing AI applications in cardiac electrophysiology. Topics of interest include AI applications for diagnosis, risk prediction, drug or gene discovery, mapping and ablation, device diagnostics and monitoring, personal wearables, the incorporation of novel datasets, and innovative modeling techniques. Additionally, we encourage the submission of articles discussing ethical and reliability considerations with developing and adopting novel AI applications into clinical practice.

Dr. Faisal F. Syed
Guest Editor

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Keywords

  • artificial intelligence
  • machine learning
  • cardiac electrophysiology
  • predictive modeling
  • personalized medicine
  • signal analysis
  • big data
  • supervised learning
  • unsupervised learning

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Published Papers (1 paper)

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7 pages, 471 KiB  
Brief Report
Comparative Diagnostic Efficacy of HeartLogic and TriageHF Algorithms in Remote Monitoring of Heart Failure: A Cohort Study
by David Ledesma Oloriz, Daniel García Iglesias, Rodrigo Ariel di Massa Pezzutti, Fernando López Iglesias and José Manuel Rubín López
J. Cardiovasc. Dev. Dis. 2025, 12(6), 209; https://doi.org/10.3390/jcdd12060209 - 31 May 2025
Viewed by 698
Abstract
Introduction: Implantable defibrillator devices (ICDs) can be used for remote monitoring of different variables, including some related to Heart Failure (HF). Two different algorithms (TriageHF and HeartLogic) arise by combining some of these variables to generate an estimation of HF decompensation risk in [...] Read more.
Introduction: Implantable defibrillator devices (ICDs) can be used for remote monitoring of different variables, including some related to Heart Failure (HF). Two different algorithms (TriageHF and HeartLogic) arise by combining some of these variables to generate an estimation of HF decompensation risk in the following days. Until now, no other trial has evaluated both algorithms in a head-to-head comparison. The primary objective is to compare diagnostic accuracy of both algorithms in a similar cohort of patients. Material and Methods: Descriptive monocentric cohort study of a series of 64 patients who have been implanted with a Medtronic or Boston Scientific ICD with the TriageHF or Heart Logic algorithm available during the period between January 2020 and June 2022, with a total of 27 patients in the HeartLogic group and 37 patients in the TriageHF group. Results: During the period of the study there were a total of 1142 alarms analyzed. There were no differences in the basal characteristics of both groups. We reported a risk alarm–patient ratio of 1.31 ± 1.89 in the HeartLogic group and of 3.32 ± 3.08 in the TriageHF group (p < 0.01). In the TriageHF group, we reported a lower specificity with (0.76), with higher sensitivity (0.97) and PPV (0.18), and similar NPV (1). Survival analysis shows no statistical differences between both algorithms in the 30 days following the alert. Conclusions: TriageHF algorithm had higher sensibility and PPV, leading to a higher number of alerts/patients, while HeartLogic algorithm had a better specificity. These differences should be considered to optimize patient follow-ups in home monitoring. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiac Electrophysiology)
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