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. 2021 Jul 2;8:655252. doi: 10.3389/fcvm.2021.655252

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Copyright © 2021 O'Brien, Whitaker, Singh Sidhu, Gould, Kurzendorfer, O'Neill, Rajani, Grigoryan, Rinaldi, Taylor, Rhode, Mountney and Niederer.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ROC curves displaying the performance on the validation [magnetic resonance imaging (MRI) dataset] calculated across all 10 folds of cross validation using the optimum hyperparameter tuning. Performance of the independent computed tomography angiography (CTA) test set using the same network, trained with the MRI dataset only, is also shown. ROC curves were generated using the Scikit-learn (28) implementation varying the threshold required for a positive classification. Confidence is shown with 1 standard deviation from the mean sensitivity and specificity values shown for each curve as calculated using bootstrapping with replacement.