Bibliometric analysis of research on coronavirus disease 2019 imaging in China_West China Medical Journal

Authors：

OU Peiling , WEN Ru , SHI Linfeng , WANG Jian ,  LIU Chen

Department of Radiology, the First Affiliated Hospital of Army Medical University, Chongqing 400000, P. R. China;

Corresponding author：

LIU Chen, Email: liuchen@aifmri.com

Keywords：

Coronavirus disease 2019; medical imaging; “long COVID” syndrome; bibliometric analysis

DOI：

10.7507/1002-0179.202303134

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To conduct a systematic and comprehensive bibliometric analysis related to medical imaging in coronavirus disease 2019 (COVID-19) published by Chinese authors, and indicate the current status and guidance for coping with the challenges brought by long COVID. Methods Literature records pertaining to COVID-19 imaging were retrieved from the Web of Science (WOS) and Chinese Science Citation Database (CSCD) with a date range of January 1st, 2020 to December 31st, 2022. CiteSpace was used to analyze the popular topics and generate a visual map to further refine research trends. Results The search enrolled 2229 publications (1771 publications from WOS and 458 from CSCD). The monthly volume of publications showed a significant increase followed by a gradual decline, and then maintained a stable level. Clustering analysis of keywords and co-citations revealed that the research hotspots in COVID-19 imaging were clinical features, imaging differential diagnosis, and the application of artificial intelligence. Keyword burst analysis showed that “deep learning” was the most intensive keyword, while “attention mechanism” had the longest burst duration. Conclusions In recent years, the research in the field of COVID-19 imaging has made steady progress. Artificial intelligence has received the most attention. Clinical features, imaging features and artificial intelligence assisted diagnosis are the top of most concerns. The future focus of COVID-19 imaging research may be on improving algorithm models to expand the application of artificial intelligence in disease diagnosis, treatment, and management.

Citation： OU Peiling, WEN Ru, SHI Linfeng, WANG Jian, LIU Chen. Bibliometric analysis of research on coronavirus disease 2019 imaging in China. West China Medical Journal, 2023, 38(8): 1203-1210. doi: 10.7507/1002-0179.202303134 Copy

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2.	Kandeel M, Mohamed MEM, Abd El-Lateef HM, et al. Omicron variant genome evolution and phylogenetics. J Med Virol, 2022, 94(4): 1627-1632.
3.	Nalbandian A, Sehgal K, Gupta A, et al. Post-acute COVID-19 syndrome. Nat Med, 2021, 27(4): 601-615.
4.	Ran S, Yao J, Liu B. The association between COVID-19 and cognitive performance: a Mendelian randomization analysis. Alzheimers Dement, 2023, 19(6): 2742-2744.
5.	Li K, Fang Y, Li W, et al. CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19). Eur Radiol, 2020, 30(8): 4407-4416.
6.	Pan Y, Guan H, Zhou S, et al. Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China. Eur Radiol, 2020, 30(6): 3306-3309.
7.	Benameur N, Mahmoudi R, Zaid S, et al. SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: a review. Clin Imaging, 2021, 76: 6-14.
8.	Wilson M, Sampson M, Barrowman N, et al. Bibliometric analysis of neurology articles published in general medicine journals. JAMA Netw Open, 2021, 4(4): e215840.
9.	Chen C. A Glimpse of the first eight months of the COVID-19 literature on microsoft academic graph: themes, citation contexts, and uncertainties. Front Res Metr Anal, 2020, 5: 607286.
10.	张泽华, 郭姗姗, 赵志刚, 等. 基于 CiteSpace 的新冠肺炎研究文献计量分析. 中国医院药学杂志, 2020, 40(19): 2029-2035.
11.	Xu G, Jin B, Xian X, et al. Evolutions in the management of hepatocellular carcinoma over last 4 decades: an analysis from the 100 most influential articles in the field. Liver Cancer, 2021, 10(2): 137-150.
12.	Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet, 2020, 395(10223): 497-506.
13.	Ai T, Yang Z, Hou H, et al. Correlation of chest ct and rt-pcr testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology, 2020, 296(2): E32-E40.
14.	Pan F, Ye T, Sun P, et al. Time course of lung changes at chest CT during recovery from coronavirus disease 2019 (COVID-19). Radiology, 2020, 295(3): 715-721.
15.	Alghamdi HS, Amoudi G, Elhag S, et al. Deep learning approaches for detecting COVID-19 from chest X-ray images: a survey. IEEE Access, 2021, 9: 20235-20254.
16.	Li L, Qin L, Xu Z, et al. Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology, 2020, 296(2): E65-E71.
17.	Wang J, Liu C, Li J, et al. iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients. NPJ Digit Med, 2021, 4(1): 124.
18.	刘士远. 医学影像人工智能发展趋势与挑战. 中华放射学杂志, 2021, 55(7): 700-702.
19.	Jin C, Chen W, Cao Y, et al. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun, 2020, 11(1): 5088.
20.	Li L, Gou CY, Li XM, et al. Effects of Chinese medicine on symptoms, syndrome evolution, and lung inflammation absorption in COVID-19 convalescent patients during 84-day follow-up after hospital discharge: a prospective cohort and nested case-control study. Chin J Integr Med, 2021, 27(4): 245-251.
21.	董国菊, 王冰, 李浩. 中西医结合治疗新型冠状病毒肺炎重症患者的思考. 中国中西医结合杂志, 2020, 40(3): 269-271.

1. The Lancet. Long COVID: 3 years in. Lancet, 2023, 401(10379): 795.
2. Kandeel M, Mohamed MEM, Abd El-Lateef HM, et al. Omicron variant genome evolution and phylogenetics. J Med Virol, 2022, 94(4): 1627-1632.
3. Nalbandian A, Sehgal K, Gupta A, et al. Post-acute COVID-19 syndrome. Nat Med, 2021, 27(4): 601-615.
4. Ran S, Yao J, Liu B. The association between COVID-19 and cognitive performance: a Mendelian randomization analysis. Alzheimers Dement, 2023, 19(6): 2742-2744.
5. Li K, Fang Y, Li W, et al. CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19). Eur Radiol, 2020, 30(8): 4407-4416.
6. Pan Y, Guan H, Zhou S, et al. Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China. Eur Radiol, 2020, 30(6): 3306-3309.
7. Benameur N, Mahmoudi R, Zaid S, et al. SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: a review. Clin Imaging, 2021, 76: 6-14.
8. Wilson M, Sampson M, Barrowman N, et al. Bibliometric analysis of neurology articles published in general medicine journals. JAMA Netw Open, 2021, 4(4): e215840.
9. Chen C. A Glimpse of the first eight months of the COVID-19 literature on microsoft academic graph: themes, citation contexts, and uncertainties. Front Res Metr Anal, 2020, 5: 607286.
10. 张泽华, 郭姗姗, 赵志刚, 等. 基于 CiteSpace 的新冠肺炎研究文献计量分析. 中国医院药学杂志, 2020, 40(19): 2029-2035.
11. Xu G, Jin B, Xian X, et al. Evolutions in the management of hepatocellular carcinoma over last 4 decades: an analysis from the 100 most influential articles in the field. Liver Cancer, 2021, 10(2): 137-150.
12. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet, 2020, 395(10223): 497-506.
13. Ai T, Yang Z, Hou H, et al. Correlation of chest ct and rt-pcr testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology, 2020, 296(2): E32-E40.
14. Pan F, Ye T, Sun P, et al. Time course of lung changes at chest CT during recovery from coronavirus disease 2019 (COVID-19). Radiology, 2020, 295(3): 715-721.
15. Alghamdi HS, Amoudi G, Elhag S, et al. Deep learning approaches for detecting COVID-19 from chest X-ray images: a survey. IEEE Access, 2021, 9: 20235-20254.
16. Li L, Qin L, Xu Z, et al. Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology, 2020, 296(2): E65-E71.
17. Wang J, Liu C, Li J, et al. iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients. NPJ Digit Med, 2021, 4(1): 124.
18. 刘士远. 医学影像人工智能发展趋势与挑战. 中华放射学杂志, 2021, 55(7): 700-702.
19. Jin C, Chen W, Cao Y, et al. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun, 2020, 11(1): 5088.
20. Li L, Gou CY, Li XM, et al. Effects of Chinese medicine on symptoms, syndrome evolution, and lung inflammation absorption in COVID-19 convalescent patients during 84-day follow-up after hospital discharge: a prospective cohort and nested case-control study. Chin J Integr Med, 2021, 27(4): 245-251.
21. 董国菊, 王冰, 李浩. 中西医结合治疗新型冠状病毒肺炎重症患者的思考. 中国中西医结合杂志, 2020, 40(3): 269-271.

West China Medical Journal

Bibliometric analysis of research on coronavirus disease 2019 imaging in China

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