Purpose: Diabetic retinopathy (DR) is a leading cause of vision impairment globally. With the advancement of deep learning (DL) techniques, substantial research has emerged focusing on automated detection and classification of DR. This study presents a comprehensive bibliometric analysis of global scientific output concerning the application of deep learning in diabetic retinopathy research.
Methods: A systematic search of publications related to DL and DR was conducted using the Scopus database. VOSviewer software was employed for visualization and analysis of co-authorship networks, keyword co-occurrence, and country-wise collaboration. The study includes 1,267 publications spanning from 2012 to 2024.
Results: An upward trend in publication volume was observed, particularly from 2018 onwards. India and China emerged as the most prolific contributors, followed by the United States and the United Kingdom. The keyword co-occurrence network revealed dominant research themes such as diabetic retinopathy, machine learning, deep neural networks, fundus images, and diagnostic imaging. Co-authorship analysis identified Wong, Tien Yin, Chen, Xinjian, and He, Mingguang as central figures in the collaborative landscape. Institutional and cross-country collaborations highlighted an interdisciplinary and international research pattern.
Conclusions: The bibliometric analysis underscores the growing academic interest and international collaboration in applying deep learning to diabetic retinopathy. While research is expanding, further emphasis on clinical validation and integration into healthcare systems remains critical. This study provides researchers and policymakers with valuable insights into the evolution, trends, and gaps in this rapidly advancing domain.
Keywords: Diabetic Retinopathy; Deep Learning; Bibliometric Analysis; Co-authorship; VOSviewer; Artificial Intelligence; Medical Imaging; Machine Learning; Fundus Photography; Scopus...
How to cite this article: Kumar S, Bansal KL., Scientific Mapping of Diabetic Retinopathy and Deep Learning Research: A Bibliometric Approach. Int J Drug Deliv Technol. 2026;16(2s): 616-626; DOI: 10.25258/ijddt.16.616-626