A Machine Learning Model Using Cardiac CT and MRI Data Predicts Cardiovascular Events in Obstructive Coronary Artery Disease
Théo Pezel, Solenn Toupin, Valérie Bousson, Kenza Hamzi, Thomas Hovasse, Thierry Lefèvre, Bernard Chevalier, Thierry Unterseeh, Francesca Sanguineti, Stéphane Champagne, Hakim Benamer, Antoinette Neylon, Mariama Akodad, Tania Ah-Sing, Lounis Hamzi, Trecy Gonçalves, Antoine Léquipar, Emmanuel Gall, Alexandre Unger, Jean Guillaume Dillinger, Patrick Henry, O. Vignaux, Marc Sirol, Philippe Garot, Jérôme Garot
Abstract
value range, <.001 to .004). The ML model also exhibited good performance in the two external validation datasets (AUC, 0.84 and 0.92). Conclusion An ML model including both CCTA and stress cardiac MRI data demonstrated better performance in predicting MACE than traditional methods and existing scores in patients with newly diagnosed CAD. © RSNA, 2025