ReviewDiabetologia2022
Diabetic retinopathy screening in the emerging era of artificial intelligence.
Review in Diabetologia, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 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
41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 102 citations in OpenAlex.
- Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis.Biomedical engineering online · 2025Pooled it
- Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies.Frontiers in endocrinology · 2023Pooled it
- Artificial Intelligence in the Training of Radiology Residents: a Multicenter Randomized Controlled Trial.Journal of cancer education : the official journal of the American Association for Cancer Education · 2025Trial
- Intelligent Staging Performance of Diabetic Retinopathy Based on Fundus Fluorescein Angiography Images with Different Angiographic Phases.Bioengineering (Basel, Switzerland) · 2026Article
- Impact of artificial intelligence on the availability, accessibility, acceptability and quality of ophthalmic disease screening services: a scoping review.The British journal of ophthalmology · 2026Article
- Diabetic Retinopathy Screening in Primary Care Real Practice: Study Procedures and Baseline Characteristics from the RETINAvalid Project.Healthcare (Basel, Switzerland) · 2026Article
- Epidemiology and Associated Ocular Conditions of Visual Impairment in the United States: A Decade of Data from TriNetX US Network.Clinical ophthalmology (Auckland, N.Z.) · 2026Article
- Multimodal natural language processing in ophthalmology: bridging clinical text and medical imaging.Frontiers in medicine · 2026Review
- Artificial intelligence-based analysis of retinal vascular changes in the preclinical and early stages of diabetic retinopathy using ultra-widefield fundus imaging: an observational cross-sectional study.Frontiers in medicine · 2026Article
- Integrating Artificial Intelligence Into Telemedicine: Evidence, Challenges, and Future Directions.Cureus · 2025Review
- [Research on interdisciplinary issues of artificial intelligence medical devices].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Diabetic retinopathy-recommendations for screening and treatment.Wiener medizinische Wochenschrift (1946) · 2025Review
- Evolution of ophthalmological care in adult with diabetes in France between 2010 and 2022: a nationwide study.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Article
- An inherently interpretable AI model improves screening speed and accuracy for early diabetic retinopathy.PLOS digital health · 2025Article
- The efficacy of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis.International journal of retina and vitreous · 2025Article
- Grading of diabetic retinopathy using a pre-segmenting deep learning classification model: Validation of an automated algorithm.Acta ophthalmologica · 2025Article
- Exploring general practice staff perspectives on a teaching concept based on instruction videos for diabetic retinopathy screening - an interview study.Scandinavian journal of primary health care · 2025Article
- AI-Assisted Screening for Diabetic Retinopathy and Fundus Abnormalities in a Large-Scale Physical Examination Population.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Patient acceptance of AI-assisted diabetic retinopathy screening in primary care: findings from a questionnaire-based feasibility study.Frontiers in medicine · 2025Article
- Adverse drug events associated with insulin glargine: a real-world pharmacovigilance study based on the FAERS database.Frontiers in pharmacology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy is a frequent complication in diabetes and a leading cause of visual impairment. Regular eye screening is imperative to detect sight-threatening stages of diabetic retinopathy such as proliferative diabetic retinopathy and diabetic macular oedema in order to treat these before irreversible visual loss occurs. Screening is cost-effective and has been implemented in various countries in Europe and elsewhere. Along with optimised diabetes care, this has substantially reduced the risk of visual loss. Nevertheless, the growing number of patients with diabetes poses an increasing burden on healthcare systems and automated solutions are needed to alleviate the task of screening and improve diagnostic accuracy. Deep learning by convolutional neural networks is an optimised branch of artificial intelligence that is particularly well suited to automated image analysis. Pivotal studies have demonstrated high sensitivity and specificity for classifying advanced stages of diabetic retinopathy and identifying diabetic macular oedema in optical coherence tomography scans. Based on this, different algorithms have obtained regulatory approval for clinical use and have recently been implemented to some extent in a few countries. Handheld mobile devices are another promising option for self-monitoring, but so far they have not demonstrated comparable image quality to that of fundus photography using non-portable retinal cameras, which is the gold standard for diabetic retinopathy screening. Such technology has the potential to be integrated in telemedicine-based screening programmes, enabling self-captured retinal images to be transferred virtually to reading centres for analysis and planning of further steps. While emerging technologies have shown a lot of promise, clinical implementation has been sparse. Legal obstacles and difficulties in software integration may partly explain this, but it may also indicate that existing algorithms may not necessarily integrate well with national screening initiatives, which often differ substantially between countries.
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What OpenQuestion holds
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.