ArticleFrontiers in endocrinology2022
Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in diabetic retinopathy.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Research progress on microglial pyroptosis and inflammasomes: a comprehensive analysis.Frontiers in aging neuroscience · 2025Pooled it
- Trends and hotspots in the field of diabetic retinopathy imaging research from 2000-2023.Frontiers in medicine · 2024Pooled it
- Ultra-widefield color fundus photography in diabetic retinopathy: from panretinal assessment to multimodal integration.Frontiers in medicine · 2026Review
- Evaluating large language models for diabetic retinopathy multiple-choice question generation in clinical ophthalmic education.Frontiers in medicine · 2026Article
- Automated detection of diabetic retinopathy lesions in ultra-widefield fundus images using an attention-augmented YOLOv8 framework.Frontiers in cell and developmental biology · 2025Article
- Research Hotspots and Trends of RNA Research in Diabetic Retinopathy: Insights From Bibliometric Analysis.Endocrine, metabolic & immune disorders drug targets · 2025Review
- Artificial intelligence applied to diabetes complications: a bibliometric analysis.Frontiers in artificial intelligence · 2025Article
- A scoping review of advancements in machine learning for glaucoma: current trends and future direction.Frontiers in medicine · 2025Article
- Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis.Journal of diabetes science and technology · 2024Article
- Development and research status of intelligent ophthalmology in China.International journal of ophthalmology · 2024Review
- Color fundus photograph-based diabetic retinopathy grading via label relaxed collaborative learning on deep features and radiomics features.Frontiers in cell and developmental biology · 2024Article
- Review
- Analysis of research hotspots and trends in pediatric ophthalmopathy based on 10 years of WoSCC literature.Frontiers in pediatrics · 2024Article
- Research progress in artificial intelligence assisted diabetic retinopathy diagnosis.International journal of ophthalmology · 2023Article
- Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images.Frontiers in cell and developmental biology · 2023Review
- Evaluation of a novel deep learning based screening system for pathologic myopia.International journal of ophthalmology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.