Evidence map›Paper›PMID 36892649›Full record

ArticleEuropean radiology2023

COVID-19 imaging, where do we go from here? Bibliometric analysis of medical imaging in COVID-19.

Ru Wen, Mudan Zhang, Rui Xu, Yingming Gao, Lin Liu, Hui Chen, Xingang Wang, Wenyan Zhu, Huafang Lin, Chen Liu and 1 more

Abstract read
In one paragraph

Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Ru WenMedical College, Guizhou University, Guizhou, 550000, People's Republic of China.
Mudan ZhangGuizhou Medical University, Guiyang, Guizhou Province, 550000, People's Republic of China.
Rui XuDepartment of Medical Imaging, Guizhou Provincial People Hospital, No.83, East Zhongshan Road, Nanming District, Guizhou Province, 550000, Guiyang City, People's Republic of China.
Yingming GaoCollege of Life Science, Guizhou University, Guiyang, Guizhou Province, 550000, People's Republic of China.
Lin LiuDepartment of Respiratory Medicine, Guizhou Provincial People Hospital, Guiyang City, Guizhou Province, 550000, People's Republic of China.
Hui ChenDepartment of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), 30 Gao Tan Yan St, 400038, Chongqing, People's Republic of China.
Xingang WangDepartment of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), 30 Gao Tan Yan St, 400038, Chongqing, People's Republic of China.
Wenyan ZhuMedical Department, Yidu Cloud (Beijing) Technology Co., Ltd., Beijing, 100191, People's Republic of China.
Huafang LinMedical Department, Yidu Cloud (Beijing) Technology Co., Ltd., Beijing, 100191, People's Republic of China.
Chen LiuDepartment of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), 30 Gao Tan Yan St, 400038, Chongqing, People's Republic of China. liuchen@aifmri.com.ORCID http://orcid.org/0000-0001-5149-2496
Xianchun ZengDepartment of Medical Imaging, Guizhou Provincial People Hospital, No.83, East Zhongshan Road, Nanming District, Guizhou Province, 550000, Guiyang City, People's Republic of China. zengxianchun04@foxmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWe conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging to determine the current status and indicate possible future directions.

methodsThis research provides an analysis of Web of Science Core Collection (WoSCC) indexed articles on COVID-19 and medical imaging published between 1 January 2020 and 30 June 2022, using the search terms "COVID-19" and medical imaging terms (such as "X-ray" or "CT"). Publications based solely on COVID-19 themes or medical image themes were excluded. CiteSpace was used to identify the predominant topics and generate a visual map of countries, institutions, authors, and keyword networks.

resultsThe search included 4444 publications. The journal with the most publications was European Radiology, and the most co-cited journal was Radiology. China was the most frequently cited country in terms of co-authorship, with the Huazhong University of Science and Technology being the institution contributing with the highest number of relevant co-authorships. Research trends and leading topics included: assessment of initial COVID-19-related clinical imaging features, differential diagnosis using artificial intelligence (AI) technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis.

conclusionsThis bibliometric analysis of COVID-19-related medical imaging helps clarify the current research situation and developmental trends. Subsequent trends in COVID-19 imaging are likely to shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases. Key Points • We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging from 1 January 2020 to 30 June 2022. • Research trends and leading topics included assessment of initial COVID-19-related clinical imaging features, differential diagnosis using AI technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. • Future trends in COVID-19-related imaging are likely to involve a shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases.

Indexed as

Artificial IntelligenceCOVID-19BibliometricsCOVID-19 VaccinesDiagnostic ImagingHumansCOVID-19 VaccinesBibliometricsCiteSpaceCOVID-19Medical imagingTreads

Identifiers

PMID36892649
PMCPMC9996554

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.