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Artificial intelligence applicated in gastric cancer: A bibliometric and visual analysis CiteSpace.

作者信息

Zhang Guoyang, Song Jingjing, Feng Zongfeng, Zhao Wentao, Huang Pan, Liu Li, Zhang Yang, Su Xufeng, Wu Yukang, Cao Yi, Li Zhengrong, Jie Zhigang

机构信息

Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China.

Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China.

出版信息

Front Oncol. 2023 Jan 4;12:1075974. doi: 10.3389/fonc.2022.1075974. eCollection 2022.


DOI:10.3389/fonc.2022.1075974
PMID:36686778
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9846739/
Abstract

OBJECTIVE: This study aimed to analyze and visualize the current research focus, research frontiers, evolutionary processes, and trends of artificial intelligence (AI) in the field of gastric cancer using a bibliometric analysis. METHODS: The Web of Science Core Collection database was selected as the data source for this study to retrieve and obtain articles and reviews related to AI in gastric cancer. All the information extracted from the articles was imported to CiteSpace to conduct the bibliometric and knowledge map analysis, allowing us to clearly visualize the research hotspots and trends in this field. RESULTS: A total of 183 articles published between 2017 and 2022 were included, contributed by 201 authors from 33 countries/regions. Among them, China (47.54%), Japan (21.86%), and the USA (13.11%) have made outstanding contributions in this field, accounting fsor 82.51% of the total publications. The primary research institutions were Wuhan University, Tokyo University, and Tada Tomohiro Inst Gastroenterol and Proctol. Tada (n = 12) and Hirasawa (n = 90) were ranked first in the top 10 authors and co-cited authors, respectively. Gastrointestinal Endoscopy (21 publications; IF 2022, 9.189; Q1) was the most published journal, while Gastric Cancer (133 citations; IF 2022, 8.171; Q1) was the most co-cited journal. Nevertheless, the cooperation between different countries and institutions should be further strengthened. The most common keywords were AI, gastric cancer, and convolutional neural network. The "deep-learning algorithm" started to burst in 2020 and continues till now, which indicated that this research topic has attracted continuous attention in recent years and would be the trend of research on AI application in GC. CONCLUSIONS: Research related to AI in gastric cancer is increasing exponentially. Current research hotspots focus on the application of AI in gastric cancer, represented by convolutional neural networks and deep learning, in diagnosis and differential diagnosis and staging. Considering the great potential and clinical application prospects, the related area of AI applications in gastric cancer will remain a research hotspot in the future.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/9385f233f964/fonc-12-1075974-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/b7ea88042afa/fonc-12-1075974-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/27b9703a5c5a/fonc-12-1075974-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/5c64ffcc3544/fonc-12-1075974-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/5b381dfe9375/fonc-12-1075974-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/ac10a511925d/fonc-12-1075974-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/75172458aee0/fonc-12-1075974-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/18e74fc399bf/fonc-12-1075974-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/9385f233f964/fonc-12-1075974-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/b7ea88042afa/fonc-12-1075974-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/27b9703a5c5a/fonc-12-1075974-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/5c64ffcc3544/fonc-12-1075974-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/5b381dfe9375/fonc-12-1075974-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/ac10a511925d/fonc-12-1075974-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/75172458aee0/fonc-12-1075974-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/18e74fc399bf/fonc-12-1075974-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a76/9846739/9385f233f964/fonc-12-1075974-g008.jpg

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本文引用的文献

[1]
Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study.

Lancet Digit Health. 2022-5

[2]
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J Natl Compr Canc Netw. 2022-2

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Gastrointest Endosc. 2022-4

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Endoscopy. 2022-8

[5]
Bibliometric Analysis of Studies on Neuropathic Pain Associated With Depression or Anxiety Published From 2000 to 2020.

Front Hum Neurosci. 2021-9-6

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Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos).

Gastrointest Endosc. 2022-2

[7]
A Bibliometric Analysis of Exosomes in Cardiovascular Diseases From 2001 to 2021.

Front Cardiovasc Med. 2021-8-25

[8]
A Bibliometric Analysis of Pyroptosis From 2001 to 2021.

Front Immunol. 2021

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Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study.

Lancet Digit Health. 2021-10

[10]
Mapping Knowledge Structure and Research Frontiers of Ultrasound-Induced Blood-Brain Barrier Opening: A Scientometric Study.

Front Neurosci. 2021-7-14

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