Augmenting a prognostic deep learning system for referable diabetic retinopathy and maculopathy with synthetic retinal images.

ABSTRACT:

Background

Labelled data scarcity and class imbalance are common deep learning system (DLS) development challenges. We investigated if synthetic retinal images from a conditional cascaded diffusion model (CCDM) improves prognostic DLS (pDLS) performance for 2-year incident referable diabetic retinopathy or maculopathy (rDR/rM) prediction.

Methods

Macula images from 72,559 eyes (September 2013 to December 2019) from the UK South-East London Diabetic Eye Screening Programme (SEL-DESP) formed the development dataset, whilst 9,071 eyes were used for internal testing. Images from 2,842 eyes from Birmingham DESP were used for external testing. Prognostic DLS were augmented with ×1, ×2, and ×4 additional synthetic positive cases (pDLS-G) and compared to unaugmented (pDLS-N) and ×1

SUBMITTER: Nderitu P 

PROVIDER: S-EPMC12830830 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature