Fang Ying, Wu Yuedi, Gao Lijuan
Xiaoshan District Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang Province, China.
Front Med (Lausanne). 2025 Feb 6;12:1477351. doi: 10.3389/fmed.2025.1477351. eCollection 2025.
This study analyzed the research trends in machine learning (ML) pertaining to myocardial infarction (MI) from 2008 to 2024, aiming to identify emerging trends and hotspots in the field, providing insights into the future directions of research and development in ML for MI. Additionally, it compared the contributions of various countries, authors, and agencies to the field of ML research focused on MI.
A total of 1,036 publications were collected from the Web of Science Core Collection database. CiteSpace 6.3.R1, Bibliometrix, and VOSviewer were utilized to analyze bibliometric characteristics, determining the number of publications, countries, institutions, authors, keywords, and cited authors, documents, and journals in popular scientific fields. CiteSpace was used for temporal trend analysis, Bibliometrix for quantitative country and institutional analysis, and VOSviewer for visualization of collaboration networks.
Since the emergence of research literature on medical imaging and machine learning (ML) in 2008, interest in this field has grown rapidly, particularly since the pivotal moment in 2016. The ML and MI domains, represented by China and the United States, have experienced swift development in research after 2015, albeit with the United States significantly outperforming China in research quality (as evidenced by the higher impact factors of journals and citation counts of publications from the United States). Institutional collaborations have formed, notably between Harvard Medical School in the United States and Capital Medical University in China, highlighting the need for enhanced cooperation among domestic and international institutions. In the realm of MI and ML research, cooperative teams led by figures such as Dey, Damini, and Berman, Daniel S. in the United States have emerged, indicating that Chinese scholars should strengthen their collaborations and focus on both qualitative and quantitative development. The overall direction of MI and ML research trends toward Medicine, Medical Sciences, Molecular Biology, and Genetics. In particular, publications in "Circulation" and "Computers in Biology and Medicine" from the United States hold prominent positions in this study.
This paper presents a comprehensive exploration of the research hotspots, trends, and future directions in the field of MI and ML over the past two decades. The analysis reveals that deep learning is an emerging research direction in MI, with neural networks playing a crucial role in early diagnosis, risk assessment, and rehabilitation therapy.
本研究分析了2008年至2024年与心肌梗死（MI）相关的机器学习（ML）研究趋势，旨在识别该领域的新兴趋势和热点，为MI的ML研究与开发的未来方向提供见解。此外，还比较了各国、作者和机构对专注于MI的ML研究领域的贡献。
从Web of Science核心合集数据库中收集了总共1036篇出版物。利用CiteSpace 6.3.R1、Bibliometrix和VOSviewer分析文献计量特征，确定出版物数量、国家、机构、作者、关键词以及热门科学领域中被引用的作者、文献和期刊。CiteSpace用于时间趋势分析，Bibliometrix用于国家和机构的定量分析，VOSviewer用于合作网络可视化。
自2008年医学成像和机器学习（ML）研究文献出现以来，该领域的关注度迅速增长，特别是自2016年的关键节点之后。以中国和美国为代表的ML和MI领域在2015年之后研究发展迅速，尽管美国在研究质量方面明显优于中国（美国期刊的影响因子和出版物的被引用次数更高证明了这一点）。机构间合作已经形成，特别是美国的哈佛医学院和中国的首都医科大学之间，这凸显了国内外机构加强合作的必要性。在MI和ML研究领域，出现了由美国的迪伊（Dey）、达米尼（Damini）和伯曼（Berman，Daniel S.）等领衔的合作团队，这表明中国学者应加强合作，注重质量和数量的双重发展。MI和ML研究趋势的总体方向朝着医学、医学科学、分子生物学和遗传学发展。特别是，美国的《循环》（Circulation）和《生物医学中的计算机》（Computers in Biology and Medicine）杂志上的出版物在本研究中占据突出地位。
本文全面探讨了过去二十年中MI和ML领域的研究热点、趋势和未来方向。分析表明，深度学习是MI领域新兴的研究方向，神经网络在早期诊断、风险评估和康复治疗中发挥着关键作用。
