2000-2022年人工智能应用于食管癌领域全球研究的可视化分析

Visualization Analysis of Artificial Intelligence in Global Esophageal Cancer Research,2000-2022

背景随着近20余年人工智能(AI)在食管癌领域应用研究的骤增,出现了许多关于该研究的系统、荟萃分析等,但其仅针对AI在该领域应用的单一方面的总结研究,研究人员难以全面了解领域最新发展与研究热点。目的通过文献计量分析总结AI在食管癌领域的应用,阐明AI在食管癌领域相关研究的进展、热点和新兴趋势。方法检索Web of Science Core Collection(WoSCC)的Science Citation Index Expanded(SCI-E)数据库收录的AI应用于食管癌领域的所有英文文献,检索时间2000-01-01至2022-04-06。应用Microsoft Excel 2019、CiteSpace(5.8R3-64bit)和VOSviewer(1.6.18)对文献进行发文量、国家、作者、机构、共被引和关键词分析。结果2000-2022年共检索到AI应用于食管癌领域的文献918篇,共计引用文献总量23490篇。发文趋势:2000-2016年为迟缓期,发文量从6篇增至40篇;2017-2022年为快速增长期,发文量从62篇突增至216篇。60个国家、118家机构、5979位作者参与了AI在食管癌领域应用的研究,发文量排名前3位的国家分别是中国(306篇)、美国(238篇)、英国(113篇),机构合作强度排名前3位的分别是阿姆斯特丹大学〔连线粗细(TLS)=72〕、凯瑟琳娜医院(TLS=64)、埃因霍芬大学(TLS=53),发文量排名前3位的作者是荷兰的作者Jacques J G H M Bergman(16篇)、日本的作者Tomohiro Tada(12篇)、荷兰的作者Fons Van Der Sommen(12篇)。共被引作者39962位,共被引文献42992篇。AI应用于食管癌领域相关研究的突现关键词共33个,早期(2001-2008年):突现关键词以p53、突变为主;中期(2013-2018年):以食管癌分类、检查新技术(断层扫描)以及食管癌和不同癌症之间区分、鉴别和比较为主;近期(2019-2022年):以深度学习、卷积神经网络、机器学习在食管癌检查、诊断应用为最新前沿,且深度学习一词突现强度排在33个突现关键词首位(突现强度为13.89)。结论AI在食管癌领域的相关研究已迈入新阶段,从基因、突变逐步朝精准检查、诊断和治疗方向发展,深度学习、卷积神经网络、机器学习在食管癌检查、诊断应用为近期(2019-2022年)AI应用于食管癌领域的最新前沿。未来AI应用于食管癌的挑战可能主要集中在食管癌个体化数据收集、数据质量、数据处理规范、AI代码复现、辅助诊断可信度决策上。

Background The past nearly 20-year period has seen a sudden increase in the use of artificial intelligence(AI)in esophageal cancer research,and an emergence of many systematic reviews and meta-analyses of the research.However,most of the reviews and meta-analyses only address a single aspect in summary,making it difficult for researchers to gain a comprehensive understanding of the latest developments and research hotspots in the field.Objective To perform a bibliometric analysis of the use of AI in esophageal cancer research,and the development,hotspots and emerging trend in this field.Methods All literature in English regarding esophageal cancer research using AI included in the Science Citation Index Expanded database of the Web of Science Core Collection was searched from 2000-01-01 to 2022-04-06.Microsoft Excel 2019,CiteSpace(5.8R3-64bit)and VOSviewer(1.6.18)were used to analyze the literature for annual number of publications,country,author,institution,co-citation and keywords.Results Nine hundred and eighteen studies were retrieved,with a total of 23490 times of being cited.The number of studies published between 2000 and 2016 grew slowly(from 6 to 40),but increased rapidly between 2017 and 2022(from 62 to 216).Sixty countries,118 institutions and 5979 authors were involved in the studies.China(306 articles),the United States(238 articles)and the United Kingdom(113 articles)ranked the top three in terms of number of studies published.The top three institutions in terms of intensity of cooperation were University of Amsterdam(TLS=72),Catherine Hospital(TLS=64)and Eindhoven University of Technology(TLS=53).The top three authors in terms of number of publications were Jacques J G H M Bergman from the Netherlands(n=16),Tomohiro Tada from Japan(n=12),and Fons Van Der Sommen from the Netherlands(n=12).There were 39962 co-cited authors and 42992 co-cited studies.Thirty-three burst keywords were identified:the major burst keywords were p53 and mutations in 2001-2008(early stage),and were esophageal cancer classification,new examination techniques(tomography),differentiation,identification and comparison between esophageal cancer and other cancers in 2013-2018(middle stage),and were deep learning,convolutional neural network,and machine learning in esophageal cancer examination and diagnosis applications in 2019-2022(late stage).Among which deep learning had the highest burst intensity(burst intensity of 13.89).Conclusion AI application in esophageal cancer research has entered a new phase,moving gradually from genes and mutations toward accurate examination,diagnosis,and treatment.The latest major burst keywords in recent years(2019-2022)are deep learning,convolutional neural network,and machine learning in esophageal cancer examination and diagnosis.The future challenges to the use of AI in esophageal cancer research may include individual data collection,data quality assurance,data processing specifications,AI code reproduction,and reliability assurance of AI-assisted diagnostic decision-making.