ReviewScientifica2016
A Review on Recent Developments for Detection of Diabetic Retinopathy.
Review in Scientifica, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 127 citations in OpenAlex.
- In situ bioactive nanocoating enables transocular delivery of permeable drug for dual-location intervention of ocular surface and fundus disorders.Bioactive materials · 2027Article
- Knowledge distillation-based lightweight MobileNet model for diabetic retinopathy classification.Scientific reports · 2025Article
- Diabetic retinopathy screening using machine learning: a systematic review.BMC biomedical engineering · 2025Article
- Exploring the Therapeutic Potential of Ginseng in Diabetic Retinopathy: A Network Pharmacology and Molecular Docking Study.Applied bionics and biomechanics · 2025Article
- Article
- DR-NASNet: Automated System to Detect and Classify Diabetic Retinopathy Severity Using Improved Pretrained NASNet Model.Diagnostics (Basel, Switzerland) · 2023Article
- Enhancement of Diabetic Retinopathy Prognostication Using Deep Learning, CLAHE, and ESRGAN.Diagnostics (Basel, Switzerland) · 2023Article
- A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement.Healthcare (Basel, Switzerland) · 2023Article
- Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review.Multimedia tools and applications · 2023Article
- Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning.Journal of personalized medicine · 2022Article
- Evaluation of Morphological Changes in Retinal Vessels in Type 1 Diabetes Mellitus Patients with the Use of Adaptive Optics.Biomedicines · 2022Article
- Review
- Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion.Micromachines · 2022Article
- Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/Non-COVID-19 Frameworks Using Artificial Intelligence Paradigm: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
- RETRACTED: Liver Tumor Localization Based on YOLOv3 and 3D-Semantic Segmentation Using Deep Neural Networks.Diagnostics (Basel, Switzerland) · 2022Article
- Performance Analysis of Deep-Neural-Network-Based Automatic Diagnosis of Diabetic Retinopathy.Sensors (Basel, Switzerland) · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy is caused by the retinal micro vasculature which may be formed as a result of diabetes mellitus. Blindness may appear as a result of unchecked and severe cases of diabetic retinopathy. Manual inspection of fundus images to check morphological changes in microaneurysms, exudates, blood vessels, hemorrhages, and macula is a very time-consuming and tedious work. It can be made easily with the help of computer-aided system and intervariability for the observer. In this paper, several techniques for detecting microaneurysms, hemorrhages, and exudates are discussed for ultimate detection of nonproliferative diabetic retinopathy. Blood vessels detection techniques are also discussed for the diagnosis of proliferative diabetic retinopathy. Furthermore, the paper elaborates a discussion on the experiments accessed by authors for the detection of diabetic retinopathy. This work will be helpful for the researchers and technical persons who want to utilize the ongoing research in this area.
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.