Exploring machine learning applications in Meningioma Research (2004-2023).

Objective: This study aims to examine the trends in machine learning application to meningiomas between 2004 and 2023.
Methods: Publication data were extracted from the Science Citation Index Expanded (SCI-E) within the Web of Science Core Collection (WOSCC). Using CiteSpace 6.2.R6, a comprehensive analysis of publications, authors, cited authors, countries, institutions, cited journals, references, and keywords was conducted on December 1, 2023.
Results: The analysis included a total of 342 articles. Prior to 2007, no publications existed in this field, and the number remained modest until 2017. A significant increase occurred in publications from 2018 onwards. The majority of the top 10 authors hailed from Germany and China, with the USA also exerting substantial international influence, particularly in academic institutions. Journals from the IEEE series contributed significantly to the publications. "Deep learning," "brain tumor," and "classification" emerged as the primary keywords of focus among researchers. The developmental pattern in this field primarily involved a combination of interdisciplinary integration and the refinement of major disciplinary branches.
Conclusion: Machine learning has demonstrated significant value in predicting early meningiomas and tailoring treatment plans. Key research focuses involve optimizing detection indicators and selecting superior machine learning algorithms. Future efforts should aim to develop high-performance algorithms to drive further innovation in this field.
Competing Interests: The authors declare no conflict of interest related to the manuscript titled "Exploring the Research Trends in the Application of Machine Learning to Meningiomas (2004–2023)." The research conducted and presented in this manuscript is independent and impartial. The authors have no financial relationships with any organizations that might have an interest in the submitted work, nor were there any other relationships or activities that could appear to have influenced the submitted work. The data for the study was extracted from the Science Citation Index Expanded (SCI-E) within the Web of Science Core Collection (WOSCC), and the analysis was performed using CiteSpace 6.2.R6. The selection of data, process of analysis, and interpretation of results were conducted without any influence or input from these platforms. All authors have contributed significantly to the research and preparation of the manuscript and have approved the final version for submission to Heliyon. Additionally, all authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This statement confirms the absence of any personal, financial, or other conflicts of interest that could be construed to influence the outcomes of this research study.
(© 2024 The Authors. Published by Elsevier Ltd.)