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

Abstract

BackgroundArtificial 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.MethodsCitation 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.ResultsOverall, 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.ConclusionInitial 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.

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

“…3) vitrectomy and intraocular photocoagulation, and 4) vitreous injection of anti-VEGF drugs (Li F. et al, 2022;Wang et al, 2022).…”

“…According to the severity of DR, DR is divided into six stages: stage I, microhemangioma and small hemorrhagic spot; stage II, yellow-white rigid exudation and hemorrhagic spot; stage III, white cotton velvet spot and hemorrhagic spot; stage IV, neovascularization or vitreous hemorrhage; stage V, neovascularization and fiber proliferation; and stage VI, neovascularization and fiber proliferation, accompanied by traction retinal detachment ( Mehra et al, 2022 ; Yang et al, 2022 ). The treatment of DR mainly includes the following aspects: 1) strict control of blood glucose levels, which can slow the occurrence and progression of DR, 2) laser photocoagulation, 3) vitrectomy and intraocular photocoagulation, and 4) vitreous injection of anti-VEGF drugs ( Li F. et al, 2022 ; Wang et al, 2022 ).…”

Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis

“…17,18 A considerable number of articles have been dedicated to the application of ML techniques in the detection of eye disease-related complications. [19][20][21][22] However, a comprehensive bibliometric analysis encompassing the use of AI techniques in various aspects of diabetes research, including detection, prediction, and management, has been notably absent from the literature until now. Addressing this gap in the literature through a bibliometric review is crucial for anyone interested in identifying the most promising research areas, trends, and directions within this rapidly evolving field.…”

Evaluation of a novel deep learning based screening system for pathologic myopia

“…Among AI, the deep learning system is a sophisticated subclass that mimics the way of the human brain works and has excellent performance comparable to that of board-certified experts in the respect of massive medical image categorizing [15][16] . The deep learning related diagnosis systems have been successfully applied in the identification of diabetic retinopathy, glaucoma and other ocular diseases [17][18][19][20][21] .…”