Predicting 1, 2 and 3 year emergent referable diabetic retinopathy and maculopathy using deep learning.

ABSTRACT:

Background

Predicting diabetic retinopathy (DR) progression could enable individualised screening with prompt referral for high-risk individuals for sight-saving treatment, whilst reducing screening burden for low-risk individuals. We developed and validated deep learning systems (DLS) that predict 1, 2 and 3 year emergent referable DR and maculopathy using risk factor characteristics (tabular DLS), colour fundal photographs (image DLS) or both (multimodal DLS).

Methods

From 162,339 development-set eyes from south-east London (UK) diabetic eye screening programme (DESP), 110,837 had eligible longitudinal data, with the remaining 51,502 used for pretraining. Internal and external (Birmingham DESP, UK) test datasets included 27,996, and 6928 eyes respectively.

Results

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SUBMITTER: Nderitu P 

PROVIDER: S-EPMC11339445 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature