What type of study is this?

This is a Bibliometric Analysis study.

May 7, 2026Current Medical Imaging Formerly Current Medical Imaging Reviews0 citations

Web of Science Bibliometrics Analysis of Magnetic Resonance ImagingResearch Advances in Multiple Sclerosis

XLXiaoxing Li DPDingbang Peng XLXiao Liang

Key Points

To conduct a bibliometric analysis of magnetic resonance imaging applications in multiple sclerosis research.
Analyzed 8,038 publications from the Web of Science Core Collection.
Used VOSviewer, Bibliometrix, and CiteSpace for data visualization and analysis.
Machine learning clustering quantified collaboration networks.
Publication growth displayed three phases: steady, accelerated, and stabilization.
Artificial intelligence and deep learning are driving paradigm shifts in MS-MRI research.
The USA led publication output while the UK excelled in international collaboration.

Abstract

INTRODUCTION: Comprehensive bibliometric analysis of magnetic resonance imaging applications in multiple sclerosis research remains scarce despite exponential growth. This study maps 25-year global MS-MRI trends (2000-2024) to identify transformative shifts. METHODS: We analyzed 8,038 publications from the Web of Science Core Collection using VOSviewer, Bibliometrix, and CiteSpace. Machine learning clustering quantified collaboration networks, while dual-map overlays and burst detection quantified interdisciplinary bridges and paradigm shifts. RESULTS: Publication growth showed three phases: steady (2005-2011, +6.2%/year), accelerated (2011-2021, peak 480 publications), and stabilization (2022-2024), with recent decline linked to diagnostic criteria simplification and artificial intelligence-driven consolidation. The USA dominated total output (24.2%), while the UK led international collaboration (44.2% multi-country publications). China's unique focus on psychoneuroimmunology contrasts with Western clinical-translational priorities. The strongest interdisciplinary link connected Neurology/Sports/Ophthalmology and Molecular/Biology/Genetics fields (Z-score = 5.3). Artificial intelligence drove paradigm shifts, with deep learning showing the highest keyword burst strength (413.27). Central authors (e.g., Massimo Filippi, Frederik Barkhof) bridged magnetic resonance imaging biomarkers and therapeutic innovation. DISCUSSION: MS-MRI research is evolving from descriptive observations to AI-driven precision medicine. Future success relies on a closed-loop paradigm integrating ultra-high-field MRI and multi-omics. CONCLUSION: This analysis reveals: (1) Magnetic resonance imaging-artificial intelligence-biomarker integration resolves clinical-radiological paradoxes, enabling dynamic patient stratification; (2) ultra-high-field magnetic resonance imaging and multi-omics provide a roadmap for precision neurology in therapy personalization; (3) global collaboration synergies may democratize advanced multiple sclerosis care.

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