Analyzing shifts in age-related macular degeneration research trends since 2014: A bibliometric study with triple-map Sankey diagrams (TMSD)

Abstract

Background: Age-related macular degeneration (AMD) is the primary cause of vision impairment in older adults, especially in developed countries. While many articles on AMD exist in the literature, none specifically delve into the trends based on document categories. While bibliometric studies typically use dual-map overlays to highlight new trends, these can become congested and unclear with standard formats (e.g., in CiteSpace software). In this study, we introduce a unique triple-map Sankey diagram (TMSD) to assess the evolution of AMD research. Our objective is to understand the nuances of AMD articles and show the effectiveness of TMSD in determining whether AMD research trends have shifted over the past decade. Methods: We collected 7465 articles and review pieces related to AMD written by ophthalmologists from the Web of Science core collection, accumulating article metadata from 2014 onward. To delve into the characteristics of these AMD articles, we employed various visualization methods, with a special focus on TMSD to track research evolution. We adopted the descriptive, diagnostic, predictive, and prescriptive analytics (DDPP) model, complemented by the follower-leading clustering algorithm (FLCA) for clustering analysis. This synergistic approach proved efficient in identifying and showcasing research focal points and budding trends using network charts within the DDPP framework. Results: Our findings indicate that: in countries, institutes, years, authors, and journals, the dominant entities were the United States, the University of Bonn in Germany, the year 2021, Dr Jae Hui Kim from South Korea, and the journal “Retina”; in accordance with the TMSD, AMD research trends have not changed significantly since 2014, as the top 4 categories for 3 citing, active, and cited articles have not changed, in sequence (Ophthalmology, Science & Technology - Other Topics, General & Internal Medicine, Pharmacology & Pharmacy). Conclusion: The introduced TMSD, which incorporates the FLCA algorithm and features in 3 columns—cited, active, and citing research categories—offers readers clearer insights into research developments compared to the traditional dual-map overlays from CiteSpace software. Such tools are especially valuable for streamlining the visualization of the intricate data often seen in bibliometric studies.