ArticleiScience2026
A systematic review on deep learning techniques for diabetic retinopathy classification in retinal fundus images.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- RetinoMamba-AnatomyQuery: Anatomy-Guided Query Learning for Efficient Vision Impairment Screening from Retinal Fundus Images.Life (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide, particularly in low- and middle-income countries. Although expert analysis of color fundus images (CFIs) enables reliable classification, this process is time-consuming. In recent years, deep learning (DL) techniques have demonstrated remarkable performance in analyzing CFI for the detection (binary classification) and multi-class classification of DR. To synthesize recent advances in this field, this study presents a systematic review, analyzing 146 peer-reviewed studies published between 2019 and 2025. This review examines lesion detection and segmentation, as well as DR detection and classification in CFI using two approaches: direct full-image and lesion-based classification. In addition, a brief scientometric analysis was performed to identify publication trends, leading journals, and countries contributing to this domain. The insights provided by this review aim to support researchers in selecting effective strategies and advancing the development of DL-based systems for the detection and classification of DR.
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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.