Work

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

Year: 2025

Type: article

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 positive-case resampled pDLS (pDLS-R) using the Area-Under-the Receiver Operating Characteristic curve (AUROC). RESULTS: Here we show that CCDM generate realistic synthetic retinal images that are comparable to real images and demonstrate the utility of synthetic retinal images in augmenting the development of a pDLS. The internal and external test AUROC for the pDLS are 0.827 (95% CI: 0.794-0.861) and 0.756 (0.680-0.831), respectively. Augmentation with ×2 additional synthetic positive cases (pDLS-G ×2) significantly improves the internal test AUROC to 0.845 (95% CI: 0.812-0.877, p = 0.044) but does not improve the external test AUROC 0.717 (0.633-0.828, p = 0.243). Resampling positive real cases alone does not improve pDLS-R performance. CONCLUSIONS: Augmenting pDLS with synthetic retinal images significantly improves pDLS performance on internal testing but not external testing suggesting further research is required to enhance the generalisability of synthetic retinal image augmentation.

Source: Communications Medicine

DOI: 10.1038/s43856-025-01316-5