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Updated: May 6, 2026
Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
Published on: October 24, 2019
Bibliometric analysis of research on the application of deep learning to ophthalmology
Min Zhao1, Haoxin Guo2, Xindan Cao1
1Department of Ophthalmology, the Fourth Affiliated Hospital of China Medical University, Shenyang, China.
Background:
Recently, deep learning has become a popular area of research, and has revolutionized the diagnosis and prediction of ocular diseases, especially fundus diseases. This study aimed to conduct a bibliometric analysis of deep learning in the field of ophthalmology to describe international research trends and examine the current research directions.
Methods:
This cross-sectional bibliometric analysis examined the development of research on deep learning in the field of ophthalmology and its sub-topics from 2015 to 2024. Visualization of similarities (VOS)-viewer was used to analyze and evaluate 3,055 articles. Data from the articles were collected on a specific date (September 11, 2024) and downloaded from the Web of Science Core Collection (WOSCC) in plain-text format.
Results:
A total of 3,055 relevant articles on the WOSCC published from 2015 to 2024 were included in this analysis. The first article on the application of deep learning to ophthalmology was published in 2015, and the number of articles on the subject has grown significantly since 2019. China was the most productive country (n=1,187), followed by the United States (n=673). Sun Yat-sen University was the institution with the most publications. Cheng and Bogunovic were the most frequently published authors. The following four different clusters were identified based on a co-occurrence cluster analysis of high-frequency keywords: (I) deep learning for the segmentation and feature extraction of ophthalmic images; (II) deep learning for the automatic detection and classification of ophthalmic images; (III) application of deep learning to ophthalmic imaging techniques; and (IV) deep learning for the diagnosis and management of ophthalmic diseases.
Conclusions:
The analysis of fundus images and the clinical application of deep learning techniques have emerged as prominent research areas in the field of ophthalmology. The substantial increase in publications and citations signifies the expanding impact and global collaboration in the application of deep learning research to ophthalmology. By identifying four distinct clusters representing sub-topics in deep learning ophthalmology research, this study contributes to the understanding of current trends and potential future advancements in the field.
