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人工智能与心房颤动：2013年至2023年的文献计量分析

Artificial intelligence and atrial fibrillation: A bibliometric analysis from 2013 to 2023.

作者信息

Jia Bochao, Chen Jiafan, Luan Yujie, Wang Huan, Wei Yi, Hu Yuanhui

机构信息

Department of Cardiovascular Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.

Beijing University of Chinese Medicine, Beijing, 100029, China.

出版信息

Heliyon. 2024 Jul 23;10(15):e35067. doi: 10.1016/j.heliyon.2024.e35067. eCollection 2024 Aug 15.

DOI:10.1016/j.heliyon.2024.e35067
PMID:39157317
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11328043/
Abstract

BACKGROUND: In the study of atrial fibrillation (AF), a prevalent cardiac arrhythmia, the utilization of artificial intelligence (AI) in diagnostic and therapeutic strategies holds the potential to address existing limitations. This research employs bibliometrics to objectively investigate research hotspots, development trends, and existing issues in the application of AI within the AF field, aiming to provide targeted recommendations for relevant researchers. METHODS: Relevant publications on the application of AI in AF field were retrieved from the Web of Science Core Collection (WoSCC) database from 2013 to 2023. The bibliometric analysis was conducted by the R (4.2.2) "bibliometrix" package and VOSviewer(1.6.19). RESULTS: Analysis of 912 publications reveals that the field of AI in AF is currently experiencing rapid development. The United States, China, and the United Kingdom have made outstanding contributions to this field. Acharya UR is a notable contributor and pioneer in the area. The following topics have been elucidated: AI's application in managing the risk of AF complications is a hot mature topic; AI-electrocardiograph for AF diagnosis and AI-assisted catheter ablation surgery are the emerging and booming topics; smart wearables for real-time AF monitoring and AI for individualized AF medication are niche and well-developed topics. CONCLUSION: This study offers comprehensive analysis of the origin, current status, and future trends of AI applications in AF, aiming to advance the development of the field.

摘要

背景：在心房颤动（AF）这一常见的心律失常研究中，人工智能（AI）在诊断和治疗策略中的应用有望解决现有局限性。本研究采用文献计量学方法，客观地探究AI在AF领域应用中的研究热点、发展趋势和存在的问题，旨在为相关研究人员提供针对性建议。 方法：从2013年至2023年的Web of Science核心合集（WoSCC）数据库中检索关于AI在AF领域应用的相关出版物。文献计量分析由R（4.2.2）“bibliometrix”软件包和VOSviewer（1.6.19）进行。 结果：对912篇出版物的分析表明，AF领域的AI目前正在迅速发展。美国、中国和英国在该领域做出了杰出贡献。阿查里亚·UR是该领域的杰出贡献者和先驱。已阐明以下主题：AI在管理AF并发症风险方面的应用是一个热门的成熟主题；用于AF诊断的AI心电图仪和AI辅助导管消融手术是新兴且蓬勃发展的主题；用于AF实时监测的智能可穿戴设备和用于AF个性化药物治疗的AI是小众但发展完善的主题。 结论：本研究对AI在AF中应用的起源、现状和未来趋势进行了全面分析，旨在推动该领域的发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b744/11328043/4d3c7be8829b/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b744/11328043/7dd9c71aa707/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b744/11328043/dfdecf3783b3/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b744/11328043/9cdb32c9b03f/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b744/11328043/958ba136c6af/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b744/11328043/7b8a7164cdf3/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b744/11328043/d4ad8377fd83/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b744/11328043/4d3c7be8829b/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b744/11328043/7dd9c71aa707/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b744/11328043/dfdecf3783b3/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b744/11328043/9cdb32c9b03f/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b744/11328043/958ba136c6af/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b744/11328043/7b8a7164cdf3/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b744/11328043/d4ad8377fd83/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b744/11328043/4d3c7be8829b/gr7.jpg

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引用本文的文献

[1]
AI-Powered Precision: Revolutionizing Atrial Fibrillation Detection with Electrocardiograms.

J Clin Med. 2025-7-11

本文引用的文献

[1]
Predicting ischemic stroke risk from atrial fibrillation based on multi-spectral fundus images using deep learning.

Front Cardiovasc Med. 2023-8-1

[2]
Global, regional, and national burden of heart failure associated with atrial fibrillation.

BMC Cardiovasc Disord. 2023-7-11

[3]
XGBoost-based machine learning test improves the accuracy of hemorrhage prediction among geriatric patients with long-term administration of rivaroxaban.

BMC Geriatr. 2023-7-10

[4]
A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022.

Diagnostics (Basel). 2023-5-13

[5]
VOSviewer and Bibliometrix.

J Med Libr Assoc. 2022-7-1

[6]
Unsupervised machine learning methods and emerging applications in healthcare.

Knee Surg Sports Traumatol Arthrosc. 2023-2

[7]
Research status and trends of the diabetic cardiomyopathy in the past 10 years (2012-2021): A bibliometric analysis.

Front Cardiovasc Med. 2022-10-20

[8]
Bibliometric analysis for economy in COVID-19 pandemic.

Heliyon. 2022-9-25

[9]
A grey zone for bibliometrics: publications indexed in Web of Science as anonymous.

Scientometrics. 2022

[10]
Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes.

Circ Arrhythm Electrophysiol. 2022-8

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