Dataset Information

A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022).

ABSTRACT:

Background

Colorectal cancer (CRC) has the third-highest incidence and second-highest mortality rate of all cancers worldwide. Early diagnosis and screening of CRC have been the focus of research in this field. With the continuous development of artificial intelligence (AI) technology, AI has advantages in many aspects of CRC, such as adenoma screening, genetic testing, and prediction of tumor metastasis.

Objective

This study uses bibliometrics to analyze research in AI in CRC, summarize the field's history and current status of research, and predict future research directions.

Method

We searched the SCIE database for all literature on CRC and AI. The documents span the period 2002-2022. we used bibliometrics to analyze the data of these papers, such as authors, countries, institutions, and references. Co-authorship, co-citation, and co-occurrence analysis were the main methods of analysis. Citespace, VOSviewer, and SCImago Graphica were used to visualize the results.

Result

This study selected 1,531 articles on AI in CRC. China has published a maximum number of 580 such articles in this field. The U.S. had the most quality publications, boasting an average citation per article of 46.13. Mori Y and Ding K were the two authors with the highest number of articles. Scientific Reports, Cancers, and Frontiers in Oncology are this field's most widely published journals. Institutions from China occupy the top 9 positions among the most published institutions. We found that research on AI in this field mainly focuses on colonoscopy-assisted diagnosis, imaging histology, and pathology examination.

Conclusion

AI in CRC is currently in the development stage with good prospects. AI is currently widely used in colonoscopy, imageomics, and pathology. However, the scope of AI applications is still limited, and there is a lack of inter-institutional collaboration. The pervasiveness of AI technology is the main direction of future housing development in this field.

SUBMITTER: Huang P

PROVIDER: S-EPMC9941644 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022).

Huang Pan P Feng Zongfeng Z Shu Xufeng X Wu Ahao A Wang Zhonghao Z Hu Tengcheng T Cao Yi Y Tu Yi Y Li Zhengrong Z

Frontiers in oncology 20230207

<h4>Background</h4>Colorectal cancer (CRC) has the third-highest incidence and second-highest mortality rate of all cancers worldwide. Early diagnosis and screening of CRC have been the focus of research in this field. With the continuous development of artificial intelligence (AI) technology, AI has advantages in many aspects of CRC, such as adenoma screening, genetic testing, and prediction of tumor metastasis.<h4>Objective</h4>This study uses bibliometrics to analyze research in AI in CRC, su ...[more]

PMID: 36824138

Similar Datasets

Project description:BackgroundExisting studies have indicated that gut microbiota is closely related to the occurrence and development of coronary heart disease(CHD). Gut microbiota and its metabolites may be important diagnostic markers for CHD in the future and are expected to become new targets for the prevention and treatment of CHD. However, the current studies exploring the link between CHD and gut microbiota are miscellaneous and poorly targeted, without bibliometric analysis available.ObjectiveThe purpose of this research was to perform a bibliometric and visual analysis of published papers on the relationship between CHD and gut microbiota. The study also sought to identify principal authors, institutions, and countries to analyze the research status and trends of gut microbiota research in the field of CHD.MethodsThe Web of Science Core Collection (WoSCC) database was searched for publications on CHD and gut microbiota between 2002 and 2022. CiteSpace 5.8. R1, VOSviewer 1.6.16, and Microsoft Excel 2019 software tools were utilized to perform this bibliometric analysis and visualization.ResultsThere were 457 qualified publications found in total, with the annual number of publications increasing. The United States dominated in this field. Hazen, Stanley l was the author of the most papers. Cleveland Clinic published the most papers of any institution. The six main clusters' specific characteristics were discovered through analysis of the co-occurrence of keywords: inflammation, diet, trimethylamine n-oxide, metabolism, cardiovascular disease, and myocardial infarction. Newly emerging research has focused predominantly on gut microbiota metabolites and recent strategies for intervention in coronary atherosclerosis.ConclusionThese results provided a useful perspective on current research and future prospects for the research on the link between CHD and gut microbiota, which may help researchers to select suitable collaborators and facilitate their research to elucidate the underlying molecular mechanisms of CHD, including the causes, prevention, and treatment.

Project description:ObjectiveThis 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.MethodsThe 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.ResultsA 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.ConclusionsResearch 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.

Project description:BackgroundSleep is an important biological process and has been linked to many diseases; however, very little is known about which and how genes control and regulate sleep. Although technology has seen significant development, this issue has still not been adequately resolved. Therefore, we conducted a bibliometric analysis to assess the progress in research on sleep quality and associated genes over the past 2 decades. Through our statistical data and discussions, we aimed to provide researchers with better research directions and ideas, thus promoting the advancement of this field.MethodsOn December 29, 2022, we utilized bibliometric techniques, such as co-cited and cluster analysis and keyword co-occurrence, using tools such as CiteSpace, VOSviewer, and the Online Analysis Platform of Literature Metrology (http://bibliometric.com/), to conduct a thorough examination of the relevant publications extracted from the Web of Science Core Collection (WoSCC). Our analysis aimed to identify the emerging trends and hot spots in this field while also predicting their potential development in future.ResultsCluster analysis of the co-cited literature revealed the most popular terms relating to sleep quality and associated genes in the manner of cluster labels; these included genome-wide association studies (GWAS), circadian rhythms, obstructive sleep apnea (OSA), DNA methylation, and depression. Keyword burst detection suggested that obstructive sleep apnea, circadian clock, circadian genes, and polygenic risk score were newly emergent research hot spots.ConclusionBased on this bibliometric analysis of the publications in the last 20 years, a comprehensive analysis of the literature clarified the contributions, changes in research hot spots, and evolution of research techniques regarding sleep quality and associated genes. This research can provide medical staff and researchers with revelations into future directions of the study on the pathological mechanisms of sleep-related diseases.

Dataset Information

A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022).

Background

Objective

Method

Result

Conclusion

Publications

A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets