ArticleFrontiers in medicine2021
Usefulness of Machine Learning for Identification of Referable Diabetic Retinopathy in a Large-Scale Population-Based Study.
Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 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
- Predicting Vision-Threatening Retinopathy in Patients With Diabetes Using Patient-Reported Data: Impact on Reading Center Workflow Efficiency.Translational vision science & technology · 2026Article
- Early Detection of Glaucoma and Diabetic Retinopathy in Low-Resource Settings: Barriers and Solutions.Journal of clinical medicine · 2026Review
- Predicting age from binarized human oral microbial data combined with an ensemble of classifiers.mSystems · 2025Article
- Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms.European journal of medical research · 2025Article
- Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography.Eye (London, England) · 2024Observational
- Predicting Implantable Collamer Lens Vault Using Machine Learning Based on Various Preoperative Biometric Factors.Translational vision science & technology · 2024Article
- Implementation of Diabetic Retinopathy Screening in Adult Patients With Type 2 Diabetes in a Primary Care Setting.Clinical diabetes : a publication of the American Diabetes Association · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
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.