Artificial intelligence in diabetic retinopathy: Bibliometric analysis

Tahmina Nasrin Poly; Md Mohaimenul Islam; Bruno Andreas Walther; Ming Chin Lin; Yu-Chuan Jack Li

doi:10.1016/j.cmpb.2023.107358

Artificial intelligence in diabetic retinopathy: Bibliometric analysis

Comput Methods Programs Biomed. 2023 Apr:231:107358. doi: 10.1016/j.cmpb.2023.107358. Epub 2023 Jan 24.

Authors

Tahmina Nasrin Poly¹, Md Mohaimenul Islam², Bruno Andreas Walther³, Ming Chin Lin⁴, Yu-Chuan Jack Li⁵

Affiliations

¹ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan; Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan.
² International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan; AESOP Technology, Songshan District, Taipei 105, Taiwan.
³ Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Am Handelshafen 12, Bremerhaven D-27570, Germany.
⁴ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235041, Taiwan; Taipei Neuroscience Institute, Taipei Medical University, Taipei 110301, Taiwan.
⁵ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan; Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan; AESOP Technology, Songshan District, Taipei 105, Taiwan. Electronic address: jack@tmu.edu.tw.

PMID: 36731310
DOI: 10.1016/j.cmpb.2023.107358

Abstract

Background: The use of artificial intelligence in diabetic retinopathy has become a popular research focus in the past decade. However, no scientometric report has provided a systematic overview of this scientific area.

Aims: We utilized a bibliometric approach to identify and analyse the academic literature on artificial intelligence in diabetic retinopathy and explore emerging research trends, key authors, co-authorship networks, institutions, countries, and journals. We further captured the diabetic retinopathy conditions and technology commonly used within this area.

Methods: Web of Science was used to collect relevant articles on artificial intelligence use in diabetic retinopathy published between January 1, 2012, and December 31, 2022 . All the retrieved titles were screened for eligibility, with one criterion that they must be in English. All the bibliographic information was extracted and used to perform a descriptive analysis. Bibliometrix (R tool) and VOSviewer (Leiden University) were used to construct and visualize the annual numbers of publications, journals, authors, countries, institutions, collaboration networks, keywords, and references.

Results: In total, 931 articles that met the criteria were collected. The number of annual publications showed an increasing trend over the last ten years. Investigative Ophthalmology & Visual Science (58/931), IEEE Access (54/931), and Computers in Biology and Medicine (23/931) were the most journals with most publications. China (211/931), India (143/931, USA (133/931), and South Korea (44/931) were the most productive countries of origin. The National University of Singapore (40/931), Singapore Eye Research Institute (35/931), and Johns Hopkins University (34/931) were the most productive institutions. Ting D. (34/931), Wong T. (28/931), and Tan G. (17/931) were the most productive researchers.

Conclusion: This study summarizes the recent advances in artificial intelligence technology on diabetic retinopathy research and sheds light on the emerging trends, sources, leading institutions, and hot topics through bibliometric analysis and network visualization. Although this field has already shown great potential in health care, our findings will provide valuable clues relevant to future research directions and clinical practice.

Keywords: Artificial intelligence; Deep learning; Diabetic retinopathy; Fundus image; Machine learning.

MeSH terms

Artificial Intelligence
Bibliometrics
China
Diabetes Mellitus*
Diabetic Retinopathy*
Humans
India