Bibliometric analysis of deep learning in chest X-ray imaging research
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,...
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| Format: | Article |
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Editorial Office of New Medicine
2023-04-01
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| Series: | Yixue xinzhi zazhi |
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| Online Access: | https://yxxz.whuznhmedj.com/futureApi/storage/attach/2304/NK2jgAjJMlP6bn5Yfz8UyROpeCT9kecskllQb5OS.pdf |
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| _version_ | 1850242753085046784 |
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| author | Xia-Xuan HUANG Yong-Mei CHEN Shi-Qi YUAN Tao HUANG Ning-Xia HE Jun LYU |
| author_facet | Xia-Xuan HUANG Yong-Mei CHEN Shi-Qi YUAN Tao HUANG Ning-Xia HE Jun LYU |
| author_sort | Xia-Xuan HUANG |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ca10fb710e484936a8fa2aa1445e1a82 |
| institution | OA Journals |
| issn | 1004-5511 |
| language | zho |
| publishDate | 2023-04-01 |
| publisher | Editorial Office of New Medicine |
| record_format | Article |
| series | Yixue xinzhi zazhi |
| spelling | doaj-art-ca10fb710e484936a8fa2aa1445e1a822025-08-20T02:00:12ZzhoEditorial Office of New MedicineYixue xinzhi zazhi1004-55112023-04-01332919910.12173/j.issn.1004-5511.2022010316346Bibliometric analysis of deep learning in chest X-ray imaging researchXia-Xuan HUANGYong-Mei CHENShi-Qi YUANTao HUANGNing-Xia HEJun LYUObjective 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.https://yxxz.whuznhmedj.com/futureApi/storage/attach/2304/NK2jgAjJMlP6bn5Yfz8UyROpeCT9kecskllQb5OS.pdfdeep learningchest x-raysciepubmedbibliometric analysiscovid-19 |
| spellingShingle | Xia-Xuan HUANG Yong-Mei CHEN Shi-Qi YUAN Tao HUANG Ning-Xia HE Jun LYU Bibliometric analysis of deep learning in chest X-ray imaging research Yixue xinzhi zazhi deep learning chest x-ray scie pubmed bibliometric analysis covid-19 |
| title | Bibliometric analysis of deep learning in chest X-ray imaging research |
| title_full | Bibliometric analysis of deep learning in chest X-ray imaging research |
| title_fullStr | Bibliometric analysis of deep learning in chest X-ray imaging research |
| title_full_unstemmed | Bibliometric analysis of deep learning in chest X-ray imaging research |
| title_short | Bibliometric analysis of deep learning in chest X-ray imaging research |
| title_sort | bibliometric analysis of deep learning in chest x ray imaging research |
| topic | deep learning chest x-ray scie pubmed bibliometric analysis covid-19 |
| url | https://yxxz.whuznhmedj.com/futureApi/storage/attach/2304/NK2jgAjJMlP6bn5Yfz8UyROpeCT9kecskllQb5OS.pdf |
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