Extended Data Fig. 3: Performance (AUPR) on 3-year incidence prediction of systemic diseases with retinal images.

a, internal evaluation, models are adapted to curated datasets from MEH-AlzEye via fine-tuning and internally evaluated on hold-out test data. b, external evaluation, models are fine-tuned on MEH-AlzEye and externally evaluated on UK Biobank. Data for internal and external evaluation is described in Supplementary Table 2. For each task, we trained the model with 5 different random seeds, determining the shuffling of training data, and evaluated the models on the test set to get 5 replicas. We derived the statistics with the 5 replicas. The error bars show 95% confidence intervals and the bars’ centre represents the mean value of the AUPR. We compare the performance of RETFound with the most competitive comparison model to check if statistically significant differences exist. p-value is calculated with the two-sided t-test and listed in the figure.