Longitudinal retinal imaging and auxiliary tasks for discrete-time survival prediction of referable diabetic retinopathy and maculopathy: a retrospective cohort study

Deep learning can predict progression to referable diabetic retinopathy (rDR) and referable maculopathy (rM) from screening photographs, but existing models ignore the prior images that screening programmes hold. We assessed whether multi-visit (MV) images and auxiliary objectives that model the future visit improve prognostic prediction. In this retrospective cohort study of macula-centred photographs from the South-East London Diabetic Eye Screening Programme (UK), eyes without referable disease at baseline and with a follow-up at 20-28 months were split into development (72,559 eyes, 36,648 patients) and test (17,917 eyes, 9,071 patients) sets. A time-aware discrete-time survival model predicted incident rDR (R2 or R3), rM (M1), or either (rDR/rM). We compared baseline-only input with up to three visits (MV), alone or with auxiliary supervision of future-visit age (MV+age) or future-visit latent prediction (MV+latent), using the AUROC of 28-month cumulative risk and paired DeLong tests with Benjamini-Hochberg correction. In the test set, 308 eyes (1.7%) developed rDR/rM, 58 (0.3%) rDR, and 275 (1.5%) rM. MV images increased the AUROC for rDR/rM from 0.749 to 0.786 (+0.037, 95% CI 0.012-0.061; q=0.011) and for rM from 0.749 to 0.788 (+0.039, 0.013-0.065; q=0.010). Auxiliary objectives did not improve MV model performance. In eyes with two or three visits, prior visits accounted for 30-38% of positive Grad-CAM attributions. Prior screening images improved prediction of incident maculopathy and the composite rDR/rM outcome, with gains driven by longitudinal images, not auxiliary supervision. External validation and prospective studies are required before the deployment of models to individualise screening.