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 752
Special Issue Editor
Interests: artificial intelligence; machine learning; cardiac electrophysiology; predictive modeling; personalized medicine; signal analysis; big data; supervised learning; unsupervised learning
Special Issues, Collections and Topics in MDPI journals
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Cardiovascular Development and Disease is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- artificial intelligence
- machine learning
- cardiac electrophysiology
- predictive modeling
- personalized medicine
- signal analysis
- big data
- supervised learning
- unsupervised learning
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