ArticleJournal of ophthalmology2019
Detection of Subclinical Diabetic Retinopathy by Fine Structure Analysis of Retinal Images.
Article in Journal of ophthalmology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed, 23 citations in OpenAlex.
- Automatic Detection and Classification of Diabetic Retinopathy Using the Improved Pooling Function in the Convolution Neural Network.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Learning for the Detection and Classification of Diabetic Retinopathy with an Improved Activation Function.Healthcare (Basel, Switzerland) · 2022Article
- Rapid statistical discrimination of fluorescence images of T cell receptors on immobilizing surfaces with different coating conditions.Scientific reports · 2021Article
- Classification of advanced and early stages of diabetic retinopathy from non-diabetic subjects by an ordinary least squares modeling method applied to OCTA images.Biomedical optics express · 2020Article
- Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network.Journal of ophthalmology · 2020Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
BACKGROUND AND
objectiveDiabetic retinopathy (DR) is a major complication of diabetes and the leading cause of blindness among US working-age adults. Detection of subclinical DR is important for disease monitoring and prevention of damage to the retina before occurrence of vision loss. The purpose of this retrospective study is to describe an automated method for discrimination of subclinical DR using fine structure analysis of retinal images.
methodsDiscrimination between nondiabetic control (NC;
resultsThe discrimination rate for subclinical DR was 88% using the automated method and higher than the rate obtained by a human observer which was 45%.
conclusionsThe method provides sensitive and rapid analysis of retinal images and could be useful in detecting subclinical DR.
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.