Tracing Global Progress: Two Decades of Age-Related Macular Degeneration Research

Abstract

Abstract Objectives. To explore age-related macular degeneration (AMD)-related research hotspots and trends from 2002 to 2022 using bibliometric analysis. Methodology. Articles on AMD published between 2002 and 2022 were collected from the Web of Science Core Collection (WoSCC). Bibliometric analysis was used to describe basic article information. Annual publication volume was analyzed using Excel 2019 software. VOSviewer 1.6.18, CiteSpace 6.1.R3, and BiblioShiny were used to extract information on countries, institutions, authors, and keywords. The information was then mapped to analyze the distribution of key research forces and collaborative relationships in the field, providing insight into the evolution of AMD research in the last two decades and its current frontiers. Results. Altogether, there were 15,452 articles on AMD research published from 2002 to 2022. The number of publications increased every year. The United States and China were the main drivers of AMD research, accounting for over 48% of all publications in the field. The University of Melbourne had the highest number of publications and the highest degree of collaboration with other institutions. Tien Y Wong was the author with the most published articles, and Frank G Holz had the highest impact. Investigative Ophthalmology and Visual Sciences was the most productive and highly cited journal. The keyword co-citation cluster analysis identified four main research areas: pathogenesis, therapy, epidemiology, and diagnosis. The keywords aflibercept, neovascular age-related macular degeneration, coherence tomography angiography, and deep learning have seen recent strong citation bursts and may become research hotspots in the future. Conclusions. Twenty years of AMD research were analyzed in this study, revealing four main areas of focus: pathogenesis, therapy, epidemiology, and diagnosis. Potential future research hotspots include aflibercept, neovascular age-related macular degeneration, coherence tomography angiography, and deep learning.