Does a machine learning model using CCTA and stress cardiac MRI data improve prediction of MACE compared to traditional methods in patients with newly diagnosed CAD?
A machine learning model combining CCTA and stress cardiac MRI data outperforms traditional methods in predicting MACE in patients with newly diagnosed CAD.
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
Pezel et al. (Wed,) studied this question.