ArticleJournal of ophthalmology2019
Severity Classification of Conjunctival Hyperaemia by Deep Neural Network Ensembles.
Article in Journal of ophthalmology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04609345 (Prevalence of Ocular Surface Disease in Malaysian Glaucoma Patients), which is not on this map. Cited by 16 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.
Prevalence of Ocular Surface Disease in Malaysian Glaucoma Patients
Who cites it
16 citing papers in PubMed, 25 citations in OpenAlex.
- A phase I study to evaluate the safety, tolerability, pharmacodynamic and pharmacokinetic profiles of ocular GLH8NDE in healthy male adults.Clinical and translational science · 2022Trial
- Trial
- Smartphone-Based Automated Quantification of Bulbar Conjunctival Hyperemia Using Vessel Density Mapping.Translational vision science & technology · 2026Article
- Conjunctival Vascular Metrics Using Automated Vessel Detection from Slit Lamp Images for Hyperemia Severity Assessment.Diagnostics (Basel, Switzerland) · 2026Article
- Grading Scales of Conjunctival Inflammation.Diagnostics (Basel, Switzerland) · 2025Review
- Toward automated assessment of conjunctival hyperemia: A semisupervised artificial intelligence approach.Annals of the New York Academy of Sciences · 2025Article
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- A Deep Learning Model for Detecting the Eyes Receiving Glaucoma Medications Using Anterior Segment Images.Translational vision science & technology · 2025Article
- Statistical Evaluation of Smartphone-Based Automated Grading System for Ocular Redness Associated with Dry Eye Disease and Implications for Clinical Trials.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Ocular Surface Disease and Dry Eye Severity in Glaucoma Patients at Urban Private Eye Care Centres in Malaysia.Clinical ophthalmology (Auckland, N.Z.) · 2024Article
- Artificial intelligence and machine learning in ophthalmology: A review.Indian journal of ophthalmology · 2023Review
- A pilot study: Quantify lung volume and emphysema extent directly from two-dimensional scout images.Medical physics · 2021Article
- Article
- Randomized, Double-Masked, Placebo-Controlled Dose Escalation Study of TAK-639 Topical Ophthalmic Solution in Subjects with Ocular Hypertension or Primary Open-Angle Glaucoma.Clinical ophthalmology (Auckland, N.Z.) · 2020Article
- Artificial intelligence in cornea and ocular surface diseases.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
- Conjunctival congestion: a novel clinical sign in older children with Tetralogy of Fallot.Discoveries (Craiova, Romania)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
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conjunctival hyperaemia is a common clinical ophthalmological finding and can be a symptom of various ocular disorders. Although several severity classification criteria have been proposed, none include objective severity criteria. Neural networks and deep learning have been utilised in ophthalmology, but not for the purpose of classifying the severity of conjunctival hyperaemia objectively. To develop a conjunctival hyperaemia grading software, we used 3700 images as the training data and 923 images as the validation test data. We trained the nine neural network models and validated the performance of these networks. We finally chose the best combination of these networks. The DenseNet201 model was the best individual model. The combination of the DenseNet201, DenseNet121, VGG19, and ResNet50 were the best model. The correlation between the multimodel responses, and the vessel-area occupied was 0.737 (
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.