Fig. 2: Cumulative dynamic areas under the receiver operating characteristic curve (cAUC) in the internal testing set. | npj Digital Medicine

Fig. 2: Cumulative dynamic areas under the receiver operating characteristic curve (cAUC) in the internal testing set.

From: Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Fig. 2: Cumulative dynamic areas under the receiver operating characteristic curve (cAUC) in the internal testing set.

The cAUCs are presented for the prediction of any adverse event (a) as well as specific events: all-cause death (b), acute coronary syndrome (c), and revascularization (d) in the internal testing dataset. Results were generated separately for each of the 10 testing folds of the cross-validation (N = 2041) and then averaged. The red, blue, and orange lines represent cAUC for the time-to-event model, perfusion abnormality, and clinical-only model, respectively. The red, blue, and orange dashed lines represent mean cAUC for the time-to-event model, perfusion abnormality, and clinical-only model, respectively. Shaded areas represent 95% confidence intervals (CI). The red arrows show the cAUC values of time-to-event deep learning averaged over the first six months after the scan (vertical dashed line).

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