| 摘要: |
| 目的：通过对2013-2023年甲状腺超声及人工智能研究方向的中英文文献进行计量、共现及可视化分析，总结该领域近期研究进展及梳理知识结构。
方法：以中国知网（CNKI）及Web of Science为来源数据库，检索关于甲状腺超声及人工智能的相关文献，利用CiteSpace分析软件进行作者、机构、关键词的图谱绘制，进行文献计量分析。
结果：共纳入文献9515篇，涉及34 个机构，119 个关键词。对发文量和发文的增长速度进行分析，知识图谱显示国内发文数量整体较国际发文量高，国内及国际发文量分别自2013、2018年起逐年上升，近两年增长速度减缓。国内发表文章较多的作者为姜珏、詹维伟、罗渝昆、周琦、雷小莹、张波等人。国际发表文章较多的作者Paul、Saba、Suri等人。国内231个机构、国际上共226个机构发表了相关文献，其中国内发文量前3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），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）。国际发文量前3的机构包括Zhejiang Univ（9篇） ，Shanghai Jiao Tong Univ（7篇），Sun Yat Sen Univ（7篇）和Huazhong Univ Sci & Technol（7篇）。机构间合作关系主要以Zhejiang Univ、Shanghai Jiao Tong Univ为核心的国内机构及以North Eastern Hill Univ为核心的国外机构。关键词分析结果显示，国内文献主要集中在多模态超声用于甲状腺结节良恶性的鉴别上，国际文献更偏向于机器深度学习，人工智能方向。
结论：通过CiteSpace进行可视化分析，发现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。 |
| 关键词: 甲状腺 超声 人工智能 CiteSpace 社会网络分析 |
| DOI： |
| 投稿时间：2024-02-26修订日期：2024-02-26 |
| 基金项目:中华国际医学交流基金会中华医学会超声医学分会首届超人新星研究基金(Z-2017-24-2305),上海交通大学数字医学研究院科研项目 |
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| Knowledge atlas of artificial intelligence of thyroid ultrasound research: Based on citespace visualization analysis |
| ZHENG Yijing,XIE Xue,XIONG Xiaoxian,BAI Xiaojun,LIU Wei,ZHENG Yuanyi |
| (Department of Ultrasound Medicine,Shanghai Sixth People''s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine) |
| Abstract: |
| Objective By conducting quantitative, co-occurrence, and visual analyses of in domestic and international literature spanning from 2013 to 2023 in the domain of thyroid ultrasound and artificial intelligence research, this study aims to summarize recent advancements in this field and organize the knowledge structure accordingly. Methods CNKI and Web of Science were used as source databases to search related literatures on thyroid ultrasound and artificial intelligence. CiteSpace analysis software was used to draw the atlas of authors, institutions and keywords, and bibliometric analysis was carried out. Results A total of 9515 publications were analyzed, involving 34 institutions and 119 keywords. The knowledge map reveals that the number of domestic publications surpasses international ones overall. Since 2013, there has been a steady increase in both domestic and international publications, with a noticeable acceleration from 2018 onwards, albeit with a recent slowdown in growth. Notably, Jiang Jue, Zhan Weiwei, Luo Yukun, Zhou Qi, Lei Xiaoying, Zhang Bo, among others, contributed significantly to the Chinese literature output, while internationally, authors such as Paul, Saba, and Suri were prolific. A total of 231 Chinese institutions and 226 international institutions published relevant literature. The top three Chinese institutions included the Ultrasound Department of Ruijin Hospital Affiliated with Shanghai Jiaotong University School of Medicine (21 articles), the Ultrasound Medical Department of Peking Union Medical College Hospital (10 articles), and the Ultrasound Laboratory of the Second Affiliated Hospital of Xi""an Jiaotong University (5 articles), along with the Ultrasound Department of Shengjing Hospital Affiliated with China Medical University (5 articles). On the international front, Zhejiang University (9 articles), Shanghai Jiao Tong University (7 articles), Sun Yat Sen University (7 articles), and Huazhong University of Science & Technology (7 articles) were among the top contributors. Inter-institutional collaboration predominantly involved domestic institutions, with Zhejiang University and Shanghai Jiao Tong University playing central roles, while North Eastern Hill University served as the core for international collaboration. Keyword analysis revealed that domestic literature predominantly focused on multi-modal ultrasound for differentiating benign and malignant thyroid nodules, whereas international literature leaned more towards machine learning, deep learning, and artificial intelligence applications. Conclusion CiteSpace visual analysis reveals a growing interest among both domestic and international researchers in the field of thyroid ultrasound artificial intelligence. However, there remains a need for enhanced multi-center collaboration across institutions, teams, and regions to facilitate deeper and more comprehensive research endeavors. |
| Key words: Thyroid Ultrasound Artificial Intelligence CiteSpace Social Network Analysis |
