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Artificial intelligence in the diagnosis and management of arrhythmias
- Source :
- European Heart Journal
- Publication Year :
- 2021
- Publisher :
- Oxford University Press (OUP), 2021.
-
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.<br />Graphical Abstract Artificial intelligence-enhanced arrhythmia care.
- Subjects :
- Big Data
education.field_of_study
Cardiac electrophysiology
business.industry
Population
Big data
Cardiac arrhythmia
Arrhythmias
Ablation
Electrophysiology
Electrocardiography
Artificial Intelligence
Artificial intelligence
Machine learning
Atrial Fibrillation
State of the Art Review
Humans
Ablation Therapy
Medicine
AcademicSubjects/MED00200
Cardiology and Cardiovascular Medicine
education
business
Internet of Things
Subjects
Details
- ISSN :
- 15229645 and 0195668X
- Volume :
- 42
- Database :
- OpenAIRE
- Journal :
- European Heart Journal
- Accession number :
- edsair.doi.dedup.....2924568480216f7f6f59e0a62216fbd3
- Full Text :
- https://doi.org/10.1093/eurheartj/ehab544