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Diabetic Retinopathy

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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Automated image curation in diabetic retinopathy screening using deep learning.

Paul Nderitu^1,2, Joan M Nunez do Rio³, Ms Laura Webster⁴

¹Section of Ophthalmology, King's College London, London, UK. p.nderitu@doctors.org.uk.

Scientific Reports

|July 1, 2022

Summary

Automated deep learning models can effectively curate diabetic retinopathy screening images, identifying laterality, retinal presence, field, and gradability. This technology offers efficient, generalizable solutions for image curation in diabetic retinopathy screening programs.

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Diabetic retinopathy (DR) screening generates heterogeneous images, including non-retinal, incorrect field, and ungradable samples.
Manual curation of these images is labor-intensive and time-consuming.
Automated methods are needed to improve the efficiency and accuracy of DR screening image curation.

Purpose of the Study:

To develop and validate deep learning (DL) models for automated curation of DR screening images.
To assess the models' ability to classify laterality, retinal presence, retinal field, and gradability.
To evaluate the generalizability of DL models across different centers and populations.

Main Methods:

Development and validation of single and multi-output deep learning classification models.
Training on an internal dataset of 7743 DR screening images (UK).
Testing on external datasets comprising 1479 images (Portugal and Paraguay).

Main Results:

High Area Under the Receiver Operating Characteristic Curve (AUROC) values for internal and external datasets across all classifications.
Excellent performance in detecting laterality (e.g., right: 0.994 vs 0.905), retinal presence (1.000 vs 1.000), retinal field (e.g., macula: 0.994 vs 0.955), and gradability (0.985 vs 0.918).
Demonstrated generalization capabilities between different centers and populations.

Conclusions:

Deep learning models effectively automate the detection of laterality, retinal presence, retinal field, and gradability in DR screening images.
These DL models show strong performance and generalizability, suggesting their utility in automated image curation.
Automated image curation using DL can streamline DR screening workflows and improve efficiency.