How did these researchers determine the confidence interval of the AUROC using resampling but without retraining the model?

In this Nature article backed by Google, the investigators develop then externally validate a deep learning model for predicting lung cancer using CT scans. All confidence intervals were computed based on the percentiles of 1,000 random resamplings (bootstraps) of the data. Confidence intervals for differences were derived by computing the metric of interest and then computing a reader–model difference on each bootstrap. When I read up how to obtain confidence intervals using the bootstrap method, what I understand is that the model must be retrained for every single bootstrap, and that the statistic is calculated for each retrained model (and the model is applied to the original pre-bootstrapped data). This implies that Google retrained their deep learning model on a bootstrap of the training sample 1000 times to obtain these intervals.