ArticleHealthcare (Basel, Switzerland)2023
A Deep Learning-Based Framework for Retinal Disease Classification.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses 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
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A review of optimization strategies for deep and machine learning in diabetic macular edema.Frontiers in artificial intelligence · 2026Pooled it
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- Leading Towards a Translation Readiness Framework: A Systematic Review of Deep Learning Approaches for Eye Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- Swin-DRNet: A robust transformer framework for diabetic retinopathy screening under heterogeneous imaging conditions.Scientific reports · 2026Article
- MidFusionEfficientV2: Improving Ophthalmic Diagnosis with Mid-Level RGB-LBP Fusion and SE Attention.Journal of clinical medicine · 2026Article
- Article
- Artificial intelligence in the diagnosis of thyroid diseases: applications and challenges.Frontiers in radiology · 2026Review
- Intelligent retinal disease detection using deep learning.Scientific reports · 2025Article
- AI-Assisted Optical Coherence Tomography Segmentation for Enhanced Diagnosis of Inherited Retinal Diseases.Translational vision science & technology · 2025Article
- Interpretable deep learning model and nomogram for predicting pathological grading of PNETs based on endoscopic ultrasound.BMC medical informatics and decision making · 2025Article
- Recent Optical Coherence Tomography (OCT) Innovations for Increased Accessibility and Remote Surveillance.Bioengineering (Basel, Switzerland) · 2025Review
- Association Between Dementia and Optical Coherence Tomography Scan Quality.Aging and disease · 2025Article
- 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
- A Beginner's Guide to Artificial Intelligence for Ophthalmologists.Ophthalmology and therapy · 2024Review
- Article
- Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images.PloS one · 2024Article
- Computational methods in glaucoma research: Current status and future outlook.Molecular aspects of medicine · 2023Review
- Optical Coherence Tomography Image Classification Using Hybrid Deep Learning and Ant Colony Optimization.Sensors (Basel, Switzerland) · 2023Article
- Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network.Bioengineering (Basel, Switzerland) · 2023Article
- Fundus image classification using feature concatenation for early diagnosis of retinal disease.Digital healthArticle
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.
Funding
Abstract
This study addresses the problem of the automatic detection of disease states of the retina. In order to solve the abovementioned problem, this study develops an artificially intelligent model. The model is based on a customized 19-layer deep convolutional neural network called VGG-19 architecture. The model (VGG-19 architecture) is empowered by transfer learning. The model is designed so that it can learn from a large set of images taken with optical coherence tomography (OCT) and classify them into four conditions of the retina: (1) choroidal neovascularization, (2) drusen, (3) diabetic macular edema, and (4) normal form. The training datasets (taken from publicly available sources) consist of 84,568 instances of OCT retinal images. The datasets exhibit all four classes of retinal disease mentioned above. The proposed model achieved a 99.17% classification accuracy with 0.995 specificities and 0.99 sensitivity, making it better than the existing models. In addition, the proper statistical evaluation is done on the predictions using such performance measures as (1) area under the receiver operating characteristic curve, (2) Cohen's kappa parameter, and (3) confusion matrix. Experimental results show that the proposed VGG-19 architecture coupled with transfer learning is an effective technique for automatically detecting the disease state of a retina.
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.