We trained our models with and without incorporating the dense LO datasets as above and tested their performance on this test set using various metrics. Firstly, we were able to reproduce the published performance values for all the architectures using their published weights except for PIGNet, whose performance was worse (correlation coefficient of 0.67 vs 0.75), Table . We found that including the LO datasets in training improved the performance of the models across all architectures, except for the 3DCNN which exhibited a slight decrease in performance. This improvement, of approximately 0.11 units in Pearson correlation coefficient for the 3DGNN model for example, suggests a positive impact of adding the dense new datasets to the model training.