ReviewEye (London, England)2019
Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy.
Review in Eye (London, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 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
54 citing papers in PubMed, 153 citations in OpenAlex.
- Clinical validation of artificial intelligence algorithms for the detection of different central-involved retinal pathologies and glaucoma from non-mydriatic images.Frontiers in artificial intelligence · 2026Trial
- Deep Learning for Diagnosis of Choroideremia andOphthalmology science · 2026Article
- RetinoMamba-AnatomyQuery: Anatomy-Guided Query Learning for Efficient Vision Impairment Screening from Retinal Fundus Images.Life (Basel, Switzerland) · 2026Article
- Multimodal Deep Learning Differentiates Papilledema and Non-Arteritic Anterior Ischemic Optic Neuropathy From Healthy Eyes.Investigative ophthalmology & visual science · 2026Article
- Multimodal deep learning for bone tumor diagnosis with clinical imaging, pathology, and blood biomarkers.Journal of bone oncology · 2025Article
- Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.Journal of diabetes and metabolic disorders · 2025Review
- [Artificial intelligence in medicine-Opportunities and risks from an ethical perspective].Die Ophthalmologie · 2025Review
- Creating a retinal image database to develop an automated screening tool for diabetic retinopathy in India.Scientific reports · 2025Article
- Diabetic Retinopathy Screening Using Smartphone-Based Fundus Photography and Deep-Learning Artificial Intelligence in the Yucatan Peninsula: A Field Study.Journal of diabetes science and technology · 2025Article
- Research progress in deep learning-based fundus image analysis for the diagnosis and prediction of hypertension-related diseases.Frontiers in cell and developmental biology · 2025Review
- CAD-EYE: An Automated System for Multi-Eye Disease Classification Using Feature Fusion with Deep Learning Models and Fluorescence Imaging for Enhanced Interpretability.Diagnostics (Basel, Switzerland) · 2024Article
- MAPLES-DR: MESSIDOR Anatomical and Pathological Labels for Explainable Screening of Diabetic Retinopathy.Scientific data · 2024Article
- Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning.Diabetes & metabolism journal · 2024Article
- Discriminating Healthy Optic Discs and Visible Optic Disc Drusen on Fundus Autofluorescence and Color Fundus Photography Using Deep Learning-A Pilot Study.Journal of clinical medicine · 2023Article
- Article
- Article
- A Review of Pathogenesis and Risk Factors of Diabetic Retinopathy With Emphasis on Screening Techniques.Cureus · 2022Review
- Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs.Eye (London, England) · 2022Article
- Which Color Channel Is Better for Diagnosing Retinal Diseases Automatically in Color Fundus Photographs?Life (Basel, Switzerland) · 2022Article
- Necessity of Local Modification for Deep Learning Algorithms to Predict Diabetic Retinopathy.International journal of environmental research and public health · 2022Article
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 3 institutions in 2 countries.
Funding
Abstract
Remarkable advances in biomedical research have led to the generation of large amounts of data. Using artificial intelligence, it has become possible to extract meaningful information from large volumes of data, in a shorter frame of time, with very less human interference. In effect, convolutional neural networks (a deep learning method) have been taught to recognize pathological lesions from images. Diabetes has high morbidity, with millions of people who need to be screened for diabetic retinopathy (DR). Deep neural networks offer a great advantage of screening for DR from retinal images, in improved identification of DR lesions and risk factors for diseases, with high accuracy and reliability. This review aims to compare the current evidences on various deep learning models for diagnosis of diabetic retinopathy (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.