Knowledge atlas of artificial intelligence of thyroid ultrasound research:a CiteSpace visualization analysis
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目的 应用CiteSpace可视化软件系统梳理近十年甲状腺超声人工智能领域的研究进展、知识结构.方法 以中国知网及Web of Science为来源数据库,检索2013～2023年关于甲状腺超声人工智能的相关文献,应用CiteSpace可视化软件绘制中、英文文献作者、机构、关键词图谱,并进行文献计量分析.结果 共纳入9515篇文献,涉及34 个机构,119 个关键词.知识图谱显示,中文文献发文量整体较英文文献发文量高,中、英文文献发文量分别自2013、2018年起逐年上升,近两年增长速度减缓.发表中文文献较多的作者包括姜珏、詹维伟、罗渝昆、周琦、雷小莹、张波等,发表英文文献较多的作者包括Paul、Saba、Suri等.国内231个机构、国际226个机构发表了相关文献,其中中文文献发文量前三的机构包括上海交通大学医学院附属瑞金医院超声科(21篇)、中国医学科学院北京协和医学院北京协和医院超声医学科(10篇)、西安交通大学第二附属医院超声研究室(5篇)和中国医科大学附属盛京医院超声科(5篇);英文文献发文量前三的机构包括浙江大学(9篇)、上海交通大学(7篇)、中山大学(7篇)和华中科技大学(7篇).机构间合作关系主要以浙江大学、上海交通大学为核心的国内机构,以及以North Eastern Hill Univ为核心的国际机构.关键词分析结果显示,中文文献主要集中在多模态超声用于甲状腺良恶性结节的鉴别方面;英文文献更偏向于机器深度学习、人工智能方向.结论 国内及国际研究人员对于甲状腺超声人工智能的关注度不断提高,但仍需加强跨机构、跨团队、跨区域的多中心协作,进一步深入研究.
Objective To systematically review the research progress and knowledge structure of artificial intelligence of thyroid ultrasound in the past decade by CiteSpace visualization software.Methods CNKI and Web of Science were used as source databases to search related literatures from 2013 to 2023 on artificial intelligence of thyroid ultrasound.CiteSpace visualization software was used to draw the atlas of authors,institutions and keywords,and bibliometric analysis was carried out.Results A total of 9515 literatures were included,involving 34 institutions and 119 keywords.The knowledge atlas showed that the number of domestic literatures was higher than that of international literatures on the whole,and the number of domestic and international literatures increased year by year since 2013 and 2018,respectively,while the growth rate decreased in the past two years.Notably,Jiang Jue,Zhan Weiwei,Luo Yukun,Zhou Qi,Lei Xiaoying,Zhang Bo,etc.contributed significantly to the domestic literatures output,while internationally,authors such as Paul,Saba,and Suri,etc.were prolific.A total of 231 domestic institutions and 226 international institutions published relevant literatures.Among them,the top three domestic institutions included the Ultrasound Department of Ruijin Hospital Affiliated with Shanghai JiaoTong University School of Medicine(21 literatures),the Department of Ultrasound Medical of Peking Union Medical College Hospital(10 literatures),the Ultrasound Laboratory of the Second Affiliated Hospital of Xi'an JiaoTong University(5 literatures)and the Ultrasound Department of Shengjing Hospital Affiliated with China Medical University(5 literatures).On the international front,Zhejiang University(9 literatures),Shanghai JiaoTong University(7 literatures),Sun Yat-Sen University(7 literatures)and Huazhong University of Science&Technology(7 literatures)were among the top contributors.Inter-institutional collaboration predominantly involved domestic institutions,with Zhejiang University and Shanghai JiaoTong University playing central roles,while North Eastern Hill University served as the core for international collaboration.The results of keyword analysis revealed that domestic literatures predominantly focused on multi-modal ultrasound for differentiating benign and malignant thyroid nodules,while international literatures leaned more towards machine deep learning and artificial intelligence applications.Conclusion Domestic and international researchers are paying increasing attention to artificial intelligence of thyroid ultrasound,but it is still necessary to enhanced multi-center collaboration across institutions,teams,and regions further in-depth research.
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