Application of artificial intelligence in glioma researches: A bibliometric analysis

Zhang, Dewei; Wang, Zhu; Guo, Jun; Chen, Wei; Gu, Xianfeng

doi:10.3389/fonc.2022.978427

Cited by 7 publications

(5 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Citation bursts for the top 10 keywords. Compared to other bibliometric analyses in oncology (11)(12)(13), most of the research also analyzed the productive authors, relevant institutions, preferred journals, and high-citation articles to summarize the research trend. However, MPNST is a relatively rare tumor and a gain of knowledge from a small number of cases is limited.…”

Section: Figurementioning

confidence: 99%

A worldwide bibliometric analysis of malignant peripheral nerve sheath tumors from 2000 to 2022

Huang

Fu²,

Gu³

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

BackgroundCurrently, malignant peripheral nerve sheath tumors (MPNST) are the subject of intense research interest. However, bibliometric studies have not been conducted in this field. The purpose of the study was to identify historical trends and presents a bibliometric analysis of the MPNST literature from 2000 to 2022.MethodsFor the bibliometric analysis, publications were retrieved from the Web of Science database based on the following search terms: [TI = (MPNST) OR TI= (malignant peripheral nerve sheath tumors) AND PY = (2000–2022)]. The following information was collected for each document: the publication trends and geographical distribution, important authors and collaboration, keyword distribution and evaluation, most popular journals, and most influential articles.ResultsWe included 1400 documents for bibliometric analysis, covering five categories: 824 articles, 17 proceedings papers, 68 letters, 402 meeting abstracts, and 89 reviews. Corrections, editorials, book chapters, data papers, publications with expressed concerns, and retractions were excluded from our research.ConclusionSince 2000, the number of publications on MPNST has continuously increased. Among all countries that contributed to the MPNST research, the USA, Japan, and China were the three most productive countries. The journal Modern Pathology has the most publications on MPNST, while those in the Cancer Research journal were the most frequently cited. The University of Texas MD Anderson Cancer Center may be a good partner to collaborate with. Recent research trends in MPNST have focused on tumorigenesis, clinical management, and predictive biomarkers.

show abstract

Section: Figurementioning

confidence: 99%

A worldwide bibliometric analysis of malignant peripheral nerve sheath tumors from 2000 to 2022

Huang

Fu²,

Gu³

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…It not only provides a basis for researchers to analyze the hotspots and development trends of the discipline and predict the direction of development of the discipline, but also provides a reference for the construction of the discipline and training of talents in the hospital. It further provides active research institutions and groups that can facilitate potential international or national research collaborations ( 15 – 18 ). Despite the rapid development of research related to the application of ML in epilepsy, there is still a lack of bibliometric analysis related to this field.…”

Section: Introductionmentioning

confidence: 99%

Machine learning applied to epilepsy: bibliometric and visual analysis from 2004 to 2023

Huo,

Luo,

et al. 2024

Front. Neurol.

View full text Add to dashboard Cite

BackgroundEpilepsy is one of the most common serious chronic neurological disorders, which can have a serious negative impact on individuals, families and society, and even death. With the increasing application of machine learning techniques in medicine in recent years, the integration of machine learning with epilepsy has received close attention, and machine learning has the potential to provide reliable and optimal performance for clinical diagnosis, prediction, and precision medicine in epilepsy through the use of various types of mathematical algorithms, and promises to make better parallel advances. However, no bibliometric assessment has been conducted to evaluate the scientific progress in this area. Therefore, this study aims to visually analyze the trend of the current state of research related to the application of machine learning in epilepsy through bibliometrics and visualization.MethodsRelevant articles and reviews were searched for 2004–2023 using Web of Science Core Collection database, and bibliometric analyses and visualizations were performed in VOSviewer, CiteSpace, and Bibliometrix (R-Tool of R-Studio).ResultsA total of 1,284 papers related to machine learning in epilepsy were retrieved from the Wo SCC database. The number of papers shows an increasing trend year by year. These papers were mainly from 1,957 organizations in 87 countries/regions, with the majority from the United States and China. The journal with the highest number of published papers is EPILEPSIA. Acharya, U. Rajendra (Ngee Ann Polytechnic, Singapore) is the authoritative author in the field and his paper “Deep Convolutional Neural Networks for Automated Detection and Diagnosis of Epileptic Seizures Using EEG Signals” was the most cited. Literature and keyword analysis shows that seizure prediction, epilepsy management and epilepsy neuroimaging are current research hotspots and developments.ConclusionsThis study is the first to use bibliometric methods to visualize and analyze research in areas related to the application of machine learning in epilepsy, revealing research trends and frontiers in the field. This information will provide a useful reference for epilepsy researchers focusing on machine learning.

show abstract

“…Bibliometric analysis is a statistical method to exhibit previous research achievements and identify research hotspots by quantitatively assess the research status of countries, institutions, authors, journals, which cannot be replaced by other methods including traditional reviews, meta-analyses (20,21). It has been applied in numerous subjects and disciplines, such as economics, agriculture, engineering, medicine and so forth (22)(23)(24)(25).…”

Section: Introductionmentioning

confidence: 99%

Global research of artificial intelligence in strabismus: a bibliometric analysis

Zhou,

Zhang,

Tang

et al. 2023

Front. Med.

View full text Add to dashboard Cite

PurposeTo analyze the global publications on artificial intelligence (AI) in strabismus using a bibliometric approach.MethodsThe Web of Science Core Collection (WoSCC) database was used to retrieve all of the publications on AI in strabismus from 2002 to 2023. We analyzed the publication and citation trend and identified highly-cited articles, prolific countries, institutions, authors and journals, relevant research domains and keywords. VOSviewer (software) and Bibliometrix (package) were used for data analysis and visualization.ResultsBy analyzing a total of 146 relevant publications, this study found an overall increasing trend in the number of annual publications and citations in the last decade. USA was the most productive country with the closest international cooperation. The top 3 research domains were Ophthalmology, Engineering Biomedical and Optics. Journal of AAPOS was the most productive journal in this field. The keywords analysis showed that “deep learning” and “machine learning” may be the hotspots in the future.ConclusionIn recent years, research on the application of AI in strabismus has made remarkable progress. The future trends will be toward optimized technology and algorithms. Our findings help researchers better understand the development of this field and provide valuable clues for future research directions.

show abstract

Application of artificial intelligence in glioma researches: A bibliometric analysis

Cited by 7 publications

References 36 publications

A worldwide bibliometric analysis of malignant peripheral nerve sheath tumors from 2000 to 2022

A worldwide bibliometric analysis of malignant peripheral nerve sheath tumors from 2000 to 2022

Machine learning applied to epilepsy: bibliometric and visual analysis from 2004 to 2023

Global research of artificial intelligence in strabismus: a bibliometric analysis

Contact Info

Product

Resources

About