Artículo

Visualization and analysis of mapping knowledge domains for optic neuritis: a bibliometric research from 2013 to 2022

Resumen

Purpose To explore the global research trends, hotspots and frontiers of optic neuritis (ON) over the past decade through qualitative and quantitative analysis of bibliometrics. Methods Publications on ON from 2013 to 2022 were retrieved from Web of Science Core Collection (WoSCC). VOSviewer and CiteSpace were mainly used to facilitate bibliometric analysis and visualization. Results A total of 3027 papers were retrieved from peer-reviewed publications and the annual research output increased over time. Neurosciences neurology was the most published area. The USA was the most productive and influential country, and in the focus of international cooperation. University College London was the most productive organization and Charite Medical University of Berlin had the largest number of cooperating partners. Paul F contributed the largest number of publications and Wingerchuk DM ranked first among the co-cited authors. Multiple Sclerosis and Related Disorders was the most prolific journal publishing ON research. The most co-cited references mainly focused on the diagnostic criteria for neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS). The keywords formed the following four clusters: the pathophysiology of MS-ON; the autoantibody markers and diagnostic criteria of NMOSD-ON and myelin oligodendrocyte glycoprotein associated disorder-ON (MOGAD-ON); the epidemiology and clinical characteristics of ON; and the treatment of ON. Conclusion This bibliometrics analysis showed a systematic view of the evolutionary process, research hotspots, and future directions of ON research. It can provide insights for ON research and valuable information for neuro-ophthalmologic specialists to evaluate research policies and promote international cooperation.
Hidayat, Erwin Yudi (57205439013); Hastuti, Khafiizh (56485990500); Muda, Azah Kamilah (23390362900)
Artificial intelligence in digital image processing: A bibliometric analysis
2025
10.1016/j.iswa.2024.200466
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212984349&doi=10.1016%2fj.iswa.2024.200466&partnerID=40&md5=14c2a82f6cb46e9efa80c050fc79c24f
Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, 50131, Indonesia; Dinus Research Group for AI in Medical Science (DREAMS), Universitas Dian Nuswantoro, Semarang, 50131, Indonesia; Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, 76100, Malaysia
All Open Access; Gold Open Access
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