Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in diabetic retinopathy

Ruoyu Wang; Guangxi Zuo; Kunke Li; Wangting Li; Zhiqiang Xuan; Yongzhao Han; Weihua Yang

doi:10.3389/fendo.2022.1036426

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in diabetic retinopathy

Front Endocrinol (Lausanne). 2022 Oct 31:13:1036426. doi: 10.3389/fendo.2022.1036426. eCollection 2022.

Authors

Ruoyu Wang¹, Guangxi Zuo², Kunke Li³, Wangting Li³, Zhiqiang Xuan⁴, Yongzhao Han⁵, Weihua Yang³

Affiliations

¹ The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China.
² The First School of Clinical Medicine, Nanjing Medical University, Nanjing, China.
³ Shenzhen Eye Hospital, Jinan University, Shenzhen, China.
⁴ Institute of Occupational Health and Radiation Protection, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.
⁵ Affiliated Jiangning Hospital, Nanjing Medical University, Nanjing, China.

Abstract

Background: Artificial intelligence (AI), which has been used to diagnose diabetic retinopathy (DR), may impact future medical and ophthalmic practices. Therefore, this study explored AI's general applications and research frontiers in the detection and gradation of DR.

Methods: Citation data were obtained from the Web of Science Core Collection database (WoSCC) to assess the application of AI in diagnosing DR in the literature published from January 1, 2012, to June 30, 2022. These data were processed by CiteSpace 6.1.R3 software.

Results: Overall, 858 publications from 77 countries and regions were examined, with the United States considered the leading country in this domain. The largest cluster labeled "automated detection" was employed in the generating stage from 2007 to 2014. The burst keywords from 2020 to 2022 were artificial intelligence and transfer learning.

Conclusion: Initial research focused on the study of intelligent algorithms used to localize or recognize lesions on fundus images to assist in diagnosing DR. Presently, the focus of research has changed from upgrading the accuracy and efficiency of DR lesion detection and classification to research on DR diagnostic systems. However, further studies on DR and computer engineering are required.

Keywords: CiteSpace; artificial intelligence; bibliometric; diabetic retinopathy; systematic analysis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Artificial Intelligence
Bibliometrics
Diabetes Mellitus*
Diabetic Retinopathy* / diagnosis
Humans
Publications
United States