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过去10年胆管癌人工智能研究趋势与热点演变：一项文献计量分析

Research trends and hotspots evolution of artificial intelligence for cholangiocarcinoma over the past 10 years: a bibliometric analysis.

作者信息

Wang Ke-Xie, Li Yu-Ting, Yang Sun-Hu, Li Feng

机构信息

Department of General Surgery, Shanghai Traditional Chinese Medicine (TCM)-INTEGRATED Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China.

出版信息

Front Oncol. 2025 Feb 13;14:1454411. doi: 10.3389/fonc.2024.1454411. eCollection 2024.

DOI:10.3389/fonc.2024.1454411
PMID:40017633
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11865243/
Abstract

OBJECTIVE

To analyze the research hotspots and potential of Artificial Intelligence (AI) in cholangiocarcinoma (CCA) through visualization.

METHODS

A comprehensive search of publications on the application of AI in CCA from January 1, 2014, to December 31, 2023, within the Web of Science Core Collection, was conducted, and citation information was extracted. CiteSpace 6.2.R6 was used for the visualization analysis of citation information.

RESULTS

A total of 736 publications were included in this study. Early research primarily focused on traditional treatment methods and care strategies for CCA, but since 2019, there has been a significant shift towards the development and optimization of AI algorithms and their application in early cancer diagnosis and treatment decision-making. China emerged as the country with the highest volume of publications, while Khon Kaen University in Thailand was the academic institution with the highest number of publications. A core group of authors involved in a dense network of international collaboration was identified. was found to be the most influential journal in the field. The disciplinary development pattern in this domain exhibits the characteristic of multiple disciplines intersecting and integrating.

CONCLUSION

The current research hotspots primarily revolve around three directions: AI in the diagnosis and classification of CCA, AI in the preoperative assessment of cancer metastasis risk in CCA, and AI in the prediction of postoperative recurrence in CCA. The complementarity and interdependence among different AI applications will facilitate future applications of AI in the CCA field.

摘要

目的

通过可视化分析胆管癌（CCA）领域中人工智能（AI）的研究热点和潜力。

方法

对2014年1月1日至2023年12月31日期间Web of Science核心合集中关于AI在CCA中应用的出版物进行全面检索，并提取引用信息。使用CiteSpace 6.2.R6对引用信息进行可视化分析。

结果

本研究共纳入736篇出版物。早期研究主要集中在CCA的传统治疗方法和护理策略上，但自2019年以来，研究重点已显著转向AI算法的开发与优化及其在早期癌症诊断和治疗决策中的应用。中国是发表量最高的国家，泰国孔敬大学是发表量最多的学术机构。确定了一个参与紧密国际合作网络的核心作者群体。发现 是该领域最具影响力的期刊。该领域的学科发展模式呈现多学科交叉融合的特点。

结论

当前的研究热点主要围绕三个方向：AI在CCA诊断和分类中的应用、AI在CCA癌症转移风险术前评估中的应用以及AI在CCA术后复发预测中的应用。不同AI应用之间的互补性和相互依赖性将促进AI在CCA领域的未来应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/21805f5e6cf2/fonc-14-1454411-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/85c9e633eb61/fonc-14-1454411-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/11afad9c87c7/fonc-14-1454411-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/324ed2ef30cc/fonc-14-1454411-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/a6ad210b0f53/fonc-14-1454411-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/767439d3d92f/fonc-14-1454411-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/60cb5603a0b5/fonc-14-1454411-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/21805f5e6cf2/fonc-14-1454411-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/85c9e633eb61/fonc-14-1454411-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/a0ca0eb6624a/fonc-14-1454411-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/ff409f0026cd/fonc-14-1454411-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/1733dbc0924d/fonc-14-1454411-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/11afad9c87c7/fonc-14-1454411-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/324ed2ef30cc/fonc-14-1454411-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/a6ad210b0f53/fonc-14-1454411-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/767439d3d92f/fonc-14-1454411-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/60cb5603a0b5/fonc-14-1454411-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a5a/11865243/21805f5e6cf2/fonc-14-1454411-g010.jpg

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