ReviewSensors (Basel, Switzerland)2022
Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis 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
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 79 citations in OpenAlex.
- Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies.Frontiers in endocrinology · 2023Pooled it
- Predicting progression to proliferative diabetic retinopathy using automated versus manual quantification of retinal haemorrhages.Eye (London, England) · 2026Article
- A vessel-guided multi-task deep learning framework with visual interpretability for simultaneous retinal vessel segmentation and multi-disease classification from fundus images.Frontiers in medicine · 2026Article
- Artificial intelligence for early detection of diabetic retinopathy: A vision transformer-based approach.PloS one · 2026Article
- Enhancing fundus image analysis for diabetic retinopathy using CheXNet with CBAM and Grad-CAM visualization.Frontiers in medicine · 2026Article
- Artificial Intelligence in Diabetic Retinopathy and Diabetic Macular Edema: A Narrative Review.Bioengineering (Basel, Switzerland) · 2025Review
- Diabetic retinopathy screening using machine learning: a systematic review.BMC biomedical engineering · 2025Article
- Assessing ResNeXt and RegNet Models for Diabetic Retinopathy Classification: A Comprehensive Comparative Study.Diagnostics (Basel, Switzerland) · 2025Article
- Graph-enhanced multimodal fusion of vascular biomarkers and deep features for diabetic retinopathy detection.Frontiers in artificial intelligence · 2025Article
- Color Fundus Photography and Deep Learning Applications in Alzheimer Disease.Mayo Clinic proceedings. Digital health · 2024Article
- Multiresolution cascaded attention U-Net for localization and segmentation of optic disc and fovea in fundus images.Scientific reports · 2024Article
- Assessing the Impact of Image Quality on Deep Learning Classification of Infectious Keratitis.Ophthalmology science · 2023Article
- Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks.Brain sciences · 2023Article
- Automatic Detection of Microaneurysms in Fundus Images Using an Ensemble-Based Segmentation Method.Sensors (Basel, Switzerland) · 2023Article
- EfficientNetV2 Based Ensemble Model for Quality Estimation of Diabetic Retinopathy Images from DeepDRiD.Diagnostics (Basel, Switzerland) · 2023Article
- Comparing the Clinical Viability of Automated Fundus Image Segmentation Methods.Sensors (Basel, Switzerland) · 2022Article
- Evaluation of Structural Retinal Layer Alterations in Retinitis Pigmentosa.Romanian journal of ophthalmologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning (DL) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple levels of abstraction. In the recent past, the use of deep learning has been proliferating, yielding promising results in applications across a growing number of fields, most notably in image processing, medical image analysis, data analysis, and bioinformatics. DL algorithms have also had a significant positive impact through yielding improvements in screening, recognition, segmentation, prediction, and classification applications across different domains of healthcare, such as those concerning the abdomen, cardiac, pathology, and retina. Given the extensive body of recent scientific contributions in this discipline, a comprehensive review of deep learning developments in the domain of diabetic retinopathy (DR) analysis, viz., screening, segmentation, prediction, classification, and validation, is presented here. A critical analysis of the relevant reported techniques is carried out, and the associated advantages and limitations highlighted, culminating in the identification of research gaps and future challenges that help to inform the research community to develop more efficient, robust, and accurate DL models for the various challenges in the monitoring and diagnosis of DR.
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.