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HomeArticlesVol 33,2023 No.2Detail

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

Published on Apr. 25, 2023Total Views: 2905 timesTotal Downloads: 1321 timesDownloadMobile

Author: Xia-Xuan HUANG 1, 2 Yong-Mei CHEN 3 Shi-Qi YUAN 1, 2 Tao HUANG 2 Ning-Xia HE 2 Jun LYU 2, 4

Affiliation: 1. Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China 2. Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China 3. Editorial Department of Journal of Jinan University, Guangzhou 510632, China 4. Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou 510632, China

Keywords: Deep learning Chest X-ray SCIE PubMed Bibliometric analysis COVID-19

DOI: 10.12173/j.issn.1004-5511.202201031

Reference: Huang XX, Chen YM, Yuan SQ, Huang T, He NX, Lyu J. Bibliometric analysis of deep learning in chest X-ray imaging research[J]. Yixue Xinzhi Zazhi, 2023, 33(2): 91-99. DOI: 10.12173/j.issn.1004-5511.202201031. [Article in Chinese]

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Abstract

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 4 409 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.

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References

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