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十七年来人工智能在肝病领域应用的全球趋势

Global trends in artificial intelligence applications in liver disease over seventeen years.

作者信息

Zhou Xue-Qin, Huang Shu, Shi Xia-Min, Liu Sha, Zhang Wei, Shi Lei, Lv Mu-Han, Tang Xiao-Wei

机构信息

Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China.

Department of Gastroenterology, Lianshui People' Hospital of Kangda College Affiliated to Nanjing Medical University, Huaian 223499, Jiangsu Province, China.

出版信息

World J Hepatol. 2025 Mar 27;17(3):101721. doi: 10.4254/wjh.v17.i3.101721.

DOI:10.4254/wjh.v17.i3.101721
PMID:40177211
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11959664/
Abstract

BACKGROUND

In recent years, the utilization of artificial intelligence (AI) technology has gained prominence in the field of liver disease.

AIM

To analyzes AI research in the field of liver disease, summarizes the current research status and identifies hot spots.

METHODS

We searched the Web of Science Core Collection database for all articles and reviews on hepatopathy and AI. The time spans from January 2007 to August 2023. We included 4051 studies for further collection of information, including authors, countries, institutions, publication years, keywords and references. VOS viewer, CiteSpace, R 4.3.1 and Scimago Graphica were used to visualize the results.

RESULTS

A total of 4051 articles were analyzed. China was the leading contributor, with 1568 publications, while the United States had the most international collaborations. The most productive institutions and journals were the and . Keywords co-occurrence analysis can be roughly summarized into four clusters: Risk prediction, diagnosis, treatment and prognosis of liver diseases. "Machine learning", "deep learning", "convolutional neural network", "CT", and "microvascular infiltration" have been popular research topics in recent years.

CONCLUSION

AI is widely applied in the risk assessment, diagnosis, treatment, and prognosis of liver diseases, with a shift from invasive to noninvasive treatment approaches.

摘要

背景

近年来，人工智能（AI）技术在肝脏疾病领域的应用日益突出。

目的

分析肝脏疾病领域的人工智能研究，总结当前研究现状并确定热点。

方法

我们在科学网核心合集数据库中搜索了所有关于肝病和人工智能的文章及综述。时间跨度为2007年1月至2023年8月。我们纳入了4051项研究以进一步收集信息，包括作者、国家、机构、发表年份、关键词和参考文献。使用VOSviewer、CiteSpace、R 4.3.1和Scimago Graphica对结果进行可视化。

结果

共分析了4051篇文章。中国是主要贡献者，有1568篇出版物，而美国的国际合作最多。产出最多的机构和期刊分别是 和 。关键词共现分析大致可分为四个集群：肝脏疾病的风险预测、诊断、治疗和预后。“机器学习”“深度学习”“卷积神经网络”“CT”和“微血管浸润”是近年来热门的研究主题。

结论

人工智能广泛应用于肝脏疾病的风险评估、诊断、治疗和预后，治疗方法正从侵入性向非侵入性转变。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/a9c01a23f826/101721-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/dd5b8d61f65f/101721-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/a7ccf82f4150/101721-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/69c4c5ff3f6e/101721-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/d3c9f0b3a366/101721-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/85d8011fa7a3/101721-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/56e5f7798412/101721-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/52af5f712813/101721-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/a9c01a23f826/101721-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/dd5b8d61f65f/101721-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/a7ccf82f4150/101721-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/69c4c5ff3f6e/101721-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/d3c9f0b3a366/101721-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/85d8011fa7a3/101721-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/56e5f7798412/101721-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/52af5f712813/101721-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9b5/11959664/a9c01a23f826/101721-g008.jpg

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本文引用的文献

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Global trends in research of mitophagy in liver diseases over past two decades: A bibliometric analysis.过去二十年肝脏疾病中线粒体自噬研究的全球趋势：一项文献计量分析。
Heliyon. 2023 Aug 5;9(8):e18843. doi: 10.1016/j.heliyon.2023.e18843. eCollection 2023 Aug.
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Artificial intelligence, machine learning, and deep learning in liver transplantation.
人工智能、机器学习和深度学习在肝移植中的应用。
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