Liu Yangli, Zhao Lei, Tu Bin, Wang Jie, He Yaqun, Jiang Rufang, Wu Xiaofeng, Wen Wen, Liu Jian
Department of Ultrasound, Clinical Medical College, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China.
Front Med (Lausanne). 2025 Jul 29;12:1587364. doi: 10.3389/fmed.2025.1587364. eCollection 2025.
BACKGROUND: Echocardiography is a cornerstone in the clinical diagnosis of cardiovascular diseases, providing critical insights into cardiac structure and function. Over recent years, artificial intelligence (AI) has emerged as a transformative adjunct to traditional echocardiographic techniques, enhancing diagnostic accuracy through innovations such as automatic view labeling, advanced image segmentation, and predictive disease modeling. The objective of this study is to explore the current status and prevailing research trends in this field from 2009 to 2024 through bibliometric analysis and to forecast future developmental trajectories. METHODS: We selected the Science Citation Index Expanded (SCI-Expanded) from the Web of Science Core Collection (WOSCC) as our primary data source and conducted a comprehensive search encompassing all articles and reviews published between 2009 and 2024 and used the online analysis platform of bibliometrics, CiteSpace and VOSviewer software to analyze countries/regions, institutions, authors, keywords, and references, used Microsoft Excel 2021 to visualize the trends of the number of articles published by year. RESULTS: Between 2009 and 2024, a total of 3,411 publications on AI applications in echocardiography were identified, including 3,000 articles (87.9%) and 411 reviews (12.1%), contributed by researchers from 100 countries/regions. China and the USA were the leading contributors in terms of publication volume. Notably, institutions such as Shanghai Jiaotong University demonstrated strong research productivity and international collaboration. ranked among the most influential journals in this domain. Keyword analysis revealed that terms such as "artificial intelligence," "machine learning," "deep learning," and "echocardiography" are central research hotspots, indicating emerging trends in the field and the potential to evolve into major areas of future investigation. CONCLUSION: Over the past decade, the integration of AI with echocardiography has become increasingly sophisticated. This study highlights the critical contributions of AI applications in echocardiography to the progression of the field and offers valuable insights for researchers embarking on future investigations.
背景:超声心动图是心血管疾病临床诊断的基石,能为心脏结构和功能提供关键见解。近年来,人工智能(AI)已成为传统超声心动图技术的变革性辅助手段,通过自动视图标记、先进图像分割和预测性疾病建模等创新提高诊断准确性。本研究的目的是通过文献计量分析探索2009年至2024年该领域的现状和主要研究趋势,并预测未来的发展轨迹。 方法:我们选择科学引文索引扩展版(SCI-Expanded)作为主要数据源,对2009年至2024年发表的所有文章和综述进行全面检索,并使用文献计量学在线分析平台CiteSpace和VOSviewer软件分析国家/地区、机构、作者、关键词和参考文献,使用Microsoft Excel 2021可视化逐年发表文章数量的趋势。 结果:2009年至2024年期间,共识别出3411篇关于人工智能在超声心动图中应用的出版物,其中包括3000篇文章(87.9%)和411篇综述(12.1%),来自100个国家/地区的研究人员参与其中。中国和美国在发表量方面是主要贡献者。值得注意的是,上海交通大学等机构展现出强大的研究生产力和国际合作。 位列该领域最具影响力的期刊之中。关键词分析表明,“人工智能”“机器学习”“深度学习”和“超声心动图”等术语是核心研究热点,表明该领域的新兴趋势以及发展成为未来主要研究领域的潜力。 结论:在过去十年中,人工智能与超声心动图的整合日益复杂。本研究突出了人工智能在超声心动图中的应用对该领域发展的关键贡献,并为未来开展研究的人员提供了有价值的见解。
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