ReviewJournal of imaging2021
Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey.
Review in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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
21 citing papers in PubMed.
- Comparative Analysis of CNN and Transformer Models for Multi-Class Diabetic Retinopathy Grading Using Fundus Images.Diagnostics (Basel, Switzerland) · 2026Article
- FISM: harnessing deep learning and reinforcement learning for precision detection of microaneurysms and retinal exudates for early diabetic retinopathy diagnosis.BioData mining · 2025Article
- Diabetic retinopathy screening using machine learning: a systematic review.BMC biomedical engineering · 2025Article
- Artificial intelligence in proliferative diabetic retinopathy: advancing diagnosis, precision surgery, and anti-VEGF therapy optimization.Frontiers in medicine · 2025Review
- Importance of OCT-derived biomarkers for the recurrence of central serous chorioretinopathy using statistics and predictive modelling.Scientific reports · 2024Article
- Review
- Blue Wavelength of Scanning Laser Ophthalmoscope Potentially Detects Arteriosclerotic Lesions in Diabetic Retinopathy.Diagnostics (Basel, Switzerland) · 2024Article
- Robust PCA withJournal of imaging · 2024Article
- The application of artificial intelligence in diabetic retinopathy: progress and prospects.Frontiers in cell and developmental biology · 2024Review
- Wavelet scattering transform application in classification of retinal abnormalities using OCT images.Scientific reports · 2023Article
- Automation of flow analysis in scleral vessels based on descriptive-associative algorithms.Scientific reports · 2023Article
- An effective and comprehensible method to detect and evaluate retinal damage due to diabetes complications.PeerJ. Computer science · 2023Article
- Optical coherence tomography image based eye disease detection using deep convolutional neural network.Health information science and systems · 2022Article
- Efficient and Robust Method to Detect the Location of Macular Center Based on Optimal Temporal Determination.Journal of imaging · 2022Article
- Frontiers in Retinal Image Processing.Journal of imaging · 2022Article
- Review
- Progress of Imaging in Diabetic Retinopathy-From the Past to the Present.Diagnostics (Basel, Switzerland) · 2022Review
- The Role of Different Retinal Imaging Modalities in Predicting Progression of Diabetic Retinopathy: A Survey.Sensors (Basel, Switzerland) · 2022Review
- Towards a Connected Mobile Cataract Screening System: A Future Approach.Journal of imaging · 2022Review
- Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy.Frontiers in endocrinology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical coherence tomography (OCT) images are used to image the retina. Next, Deep-learning (DL)-/machine-learning (ML)-based approaches make it possible to extract features from the images and to detect the presence of DR, grade its severity and segment associated lesions. This review covers the literature dealing with AI approaches to DR such as ML and DL in classification and segmentation that have been published in the open literature within six years (2016-2021). In addition, a comprehensive list of available DR datasets is reported. This list was constructed using both the PICO (P-Patient, I-Intervention, C-Control, O-Outcome) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2009 search strategies. We summarize a total of 114 published articles which conformed to the scope of the review. In addition, a list of 43 major datasets is presented.
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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.