Related Experiment Video
Updated: Jun 21, 2025
08:23
Implementation of Minimally Invasive Brain Tumor Resection in Rodents for High Viability Tissue Collection
Published on: May 9, 2022
4.3K
Exploring machine learning applications in Meningioma Research (2004-2023).
Li-Wei Zhong1, Kun-Shan Chen2, Hua-Biao Yang1
1Jiujiang Traditional Chinese Medicine Hospital, Jiujiang, Jiangxi, China.
Heliyon
|July 8, 2024
Summary
Machine learning applications for meningiomas show a significant growth trend since 2018. Research focuses on deep learning for brain tumor classification, aiming to improve early detection and treatment planning.
Area of Science:
- Bibliometrics
- Medical Informatics
- Neurosurgery
Background:
- Meningiomas represent a significant area of neurosurgical research.
- The integration of machine learning (ML) offers novel approaches to meningioma analysis.
- Understanding research trends is crucial for guiding future scientific endeavors.
Purpose of the Study:
- To analyze the publication trends of machine learning applications in meningioma research from 2004 to 2023.
- To identify key authors, institutions, countries, and journals contributing to this field.
- To determine the prominent keywords and developmental patterns in machine learning for meningiomas.
Main Methods:
- Bibliometric analysis of publications indexed in the Science Citation Index Expanded (SCI-E) within the Web of Science Core Collection (WOSCC).
- Utilized CiteSpace 6.2.R6 software for comprehensive data extraction and visualization.
- Analyzed publication volume, authors, affiliations, countries, journals, references, and keywords.
Main Results:
- A total of 342 articles were analyzed, with a notable surge in publications from 2018 onwards.
- Germany and China led in author contributions, while the USA showed substantial institutional influence.
- Keywords such as 'deep learning,' 'brain tumor,' and 'classification' were central, indicating interdisciplinary integration and disciplinary refinement.
Conclusions:
- Machine learning demonstrates considerable value in early meningioma detection and personalized treatment strategies.
- Current research priorities include optimizing detection indicators and selecting advanced ML algorithms.
- Future research should focus on developing high-performance algorithms to foster innovation in meningioma management.
