Exploring the potential of machine learning and magnetic resonance imaging in early stroke diagnosis: a bibliometric analysis (2004-2023).

Objective: To examine the focal areas of research in the early diagnosis of stroke through machine learning identification of magnetic resonance imaging characteristics from 2004 to 2023. Methods: Data were gathered from the Science Citation Index-Expanded (SCI-E) within the Web of Science Core Collection (WoSCC). Utilizing CiteSpace 6.2.R6, a thorough analysis was conducted, encompassing publications, authors, cited authors, countries, institutions, cited journals, references, and keywords. This investigation covered the period from 2004 to 2023, with the data retrieval completed on December 1, 2023, in a single day. Results: In total, 395 articles were incorporated into the analysis. Prior to 2015, the annual publication count was under 10, but a significant surge in publications was observed post-2015. Institutions and authors from the USA and China have established themselves as mature academic entities on a global scale, forging extensive collaborative networks with other institutions. High-impact journals in this field predominantly feature in top-tier publications, indicating a consensus in the medical community on the application of machine learning for early stroke diagnosis. "deep learning," "magnetic resonance imaging," and "stroke" emerged as the most attention-gathering keywords among researchers. The development in this field is marked by a coexisting pattern of interdisciplinary integration and refinement within major disciplinary branches. Conclusion: The application of machine learning in the early prediction and personalized medical plans for stroke patients using neuroimaging characteristics offers significant value. The most notable research hotspots currently are the optimal selection of neural imaging markers and the most suitable machine learning algorithm models.