深度学习运用于胸腔X射线影像研究的文献计量学分析  

Bibliometric analysis of deep learning in chest X-ray imaging research

作　　者：黄夏璇 陈咏梅 袁师其 黄韬 何宁霞 吕军 Xia-Xuan HUANG;Yong-Mei CHEN;Shi-Qi YUAN;Tao HUANG;Ning-Xia HE;Jun LYU(Department of Neurology,The First Affiliated Hospital of Jinan University,Guangzhou 510630,China;Department of Clinical Research,The First Affiliated Hospital of Jinan University,Guangzhou 510630,China;Editorial Department of Journal of Jinan University,Guangzhou 510632,China;Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization,Guangzhou 510632,China)

机构地区：[1]暨南大学附属第一医院神经内科,广州510630 [2]暨南大学附属第一医院临床研究部,广州510630 [3]暨南大学学报编辑部,广州510632 [4]广东省中医药信息化重点实验室,广州510632

出　　处：《医学新知》2023年第2期91-99,共9页New Medicine

基　　金：广东省科技计划项目(2021B1212040007)。

摘　　要：目的 基于SCIE和PubMed数据库了解深度学习在胸腔X射线影像相关领域研究文献发表情况。方法 检索2017年1月1日至2021年12月31日收录于SCIE和PubMed的关于胸腔X射线影像的文献,针对发文量、出版机构、出版期刊、引文、作者及关键词等信息进行统计分析。结果 共纳入文献440篇,深度学习在胸腔X射线影像研究相关文献发文量呈逐年增长趋势。美国发文量最多,总被引频次为4 409次,篇均被引频次为12.32次,美国的IEEE Access期刊发文量最多,达29篇。发文量排名第一的出版商是德国的Springer Nature,为83篇。核心作者7位,发文最多的有10篇,主要关键词频次出现最多的为COVID-19。结论 SCIE和PubMed收录的关于深度学习在胸腔X射线影像相关领域的文献整体呈逐年上升趋势,基本以英文文献增长为主,核心作者群尚未形成,尚未出现引文量和发文量均丰富的领军人物,高影响力文献数量有限。Objective To investigate the development of SCIE and PubMed deep learning literature on chest X-ray imaging.Methods The literature on chest X-ray images published in SCIE and PubMed from January 1,2017 to December 31,2021 was searched,and the number of articles,publishing institutions,journals,citations,authors and keywords were statistically analyzed.Results A total of 440 papers were included,and the number of papers presented an annual growth trend.The country with the largest number of papers was the United States,with a total citation frequency of 4409 times and an average citation frequency of 12.32 times.The IEEE Access in the United States published the most articles,reaching 29 articles.The number one publisher is Germany Springer Nature with 83 articles.There are 7 core authors,10 of which have published the most papers,and the most frequently cited keywords in the research content are COVID-19.Conclusion The literature on deep learning in the field of chest X-ray imaging collected in SCIE and PubMed shows an overall upward trend year by year,mainly in English.However,a core author group has not yet been formed,and there is no clear leader with prolific citations and publications,and the number of high-impact publications is still limited.

关 键 词：深度学习 胸腔X射线影像 SCIE PUBMED 文献计量学分析 新型冠状病毒肺炎 

分 类 号：G353.1[文化科学—情报学] R816.4[医药卫生—放射医学]