ReviewJournal of imaging2023
Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Review.
Review in Journal of imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 101 citations in OpenAlex.
- RetinoMamba-AnatomyQuery: Anatomy-Guided Query Learning for Efficient Vision Impairment Screening from Retinal Fundus Images.Life (Basel, Switzerland) · 2026Article
- Edge-Deployable Lightweight Deep Learning for Hypertensive Retinopathy Grading from En-Face OCT: A Patient-Level Feasibility Study.Bioengineering (Basel, Switzerland) · 2026Article
- Advanced computer-aided diagnosis for age-related macular degeneration: integrating segmentation and feature extraction for precise diagnosis.Scientific reports · 2026Article
- A Comparative Analysis of Deep Convolutional Networks for Automated Diagnosis of Retinal Detachment in Dogs.Veterinary ophthalmology · 2026Article
- Adaptive Optics RTX1 Imaging for Early Detection of Retinal Vascular Remodeling in Hypertensive Retinopathy: A Review.Journal of clinical medicine · 2026Review
- Early retinal disease detection from fundus images using deep neural networks.Scientific reports · 2026Article
- Adaptive Gating and Focal Debiasing for robust few-shot retinal disease classification.Frontiers in artificial intelligence · 2026Article
- Autofluorescence and deep learning in early disease detection: biological foundations, clinical applications, and future directions.Frontiers in artificial intelligence · 2026Review
- VG-RETFound: a hybrid retinal AI framework for early vision impairment screening through vessel-graph learning and cross-modal fusion.Frontiers in medicine · 2026Article
- Intelligent retinal disease detection using deep learning.Scientific reports · 2025Article
- A robust deep learning classifier for screening multiple retinal diseases on optical coherence tomography.Scientific reports · 2025Article
- Therapeutic potential of P2X7 receptor in retinal diseases.Purinergic signalling · 2025Review
- Re-identification of patients from imaging features extracted by foundation models.NPJ digital medicine · 2025Article
- Distributed training of foundation models for ophthalmic diagnosis.Communications engineering · 2025Article
- Tracing global progress: two decades of age-related macular degeneration research.International journal of ophthalmology · 2025Article
- Bibliometric analysis of ophthalmic OCT and OCT angiography research trends over the past 20 years.International ophthalmology · 2024Article
- A Comprehensive Review of AI Diagnosis Strategies for Age-Related Macular Degeneration (AMD).Bioengineering (Basel, Switzerland) · 2024Review
- When Two Eyes Don't Suffice-Learning Difficult Hyperfluorescence Segmentations in Retinal Fundus Autofluorescence Images via Ensemble Learning.Journal of imaging · 2024Article
- Article
- Deep Learning and The Retina: A New Frontier in Multiple Sclerosis Diagnosis.Current health sciences journalReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Millions of people are affected by retinal abnormalities worldwide. Early detection and treatment of these abnormalities could arrest further progression, saving multitudes from avoidable blindness. Manual disease detection is time-consuming, tedious and lacks repeatability. There have been efforts to automate ocular disease detection, riding on the successes of the application of Deep Convolutional Neural Networks (DCNNs) and vision transformers (ViTs) for Computer-Aided Diagnosis (CAD). These models have performed well, however, there remain challenges owing to the complex nature of retinal lesions. This work reviews the most common retinal pathologies, provides an overview of prevalent imaging modalities and presents a critical evaluation of current deep-learning research for the detection and grading of glaucoma, diabetic retinopathy, Age-Related Macular Degeneration and multiple retinal diseases. The work concluded that CAD, through deep learning, will increasingly be vital as an assistive technology. As future work, there is a need to explore the potential impact of using ensemble CNN architectures in multiclass, multilabel tasks. Efforts should also be expended on the improvement of model explainability to win the trust of clinicians and patients.
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.