ArticleScientific reports2022
Automated image curation in diabetic retinopathy screening using deep learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Predicting 1, 2 and 3 year emergent referable diabetic retinopathy and maculopathy using deep learning.Communications medicine · 2024Article
- Image quality assessment of retinal fundus photographs for diabetic retinopathy in the machine learning era: a review.Eye (London, England) · 2024Review
- On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks.Archives of computational methods in engineering : state of the art reviews · 2023Article
- Developing Laterality-Specific Computable Phenotypes from Electronic Health Record Data, Employing Treatment-Warranted Diabetic Macular Edema as a Use Case.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Diabetic retinopathy (DR) screening images are heterogeneous and contain undesirable non-retinal, incorrect field and ungradable samples which require curation, a laborious task to perform manually. We developed and validated single and multi-output laterality, retinal presence, retinal field and gradability classification deep learning (DL) models for automated curation. The internal dataset comprised of 7743 images from DR screening (UK) with 1479 external test images (Portugal and Paraguay). Internal vs external multi-output laterality AUROC were right (0.994 vs 0.905), left (0.994 vs 0.911) and unidentifiable (0.996 vs 0.680). Retinal presence AUROC were (1.000 vs 1.000). Retinal field AUROC were macula (0.994 vs 0.955), nasal (0.995 vs 0.962) and other retinal field (0.997 vs 0.944). Gradability AUROC were (0.985 vs 0.918). DL effectively detects laterality, retinal presence, retinal field and gradability of DR screening images with generalisation between centres and populations. DL models could be used for automated image curation within DR screening.
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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.