Fig. 2. Diagnostic performance overview.

ROC and PR-curve. Predictive performance of our deep learning-based approach (CARPE_ECG), a random forest based on clinical data (CARPE_Clin.), the cardiologist, and ST depression in terms of mean performance ± standard deviation (envelopes) over n = 25 bootstrap draws. The upper plots show that both machine learning approaches outperform the cardiologist in terms of area under the receiver operating characteristic and precision-recall curve. In regions of high specificity (inline plot), the neural network is on par with the cardiologist while CARPE_Clin. exhibits worse performance. Both machine learning methods outperform the cardiologist’s judgement in regions of high sensitivity (inline plot). Decision Curve: First row: Net benefit⁴³ plot for CARPE_ECG (green), CARPE_Clin. (orange), the cardiologist (purple), a myocardial perfusion scan (MPS) for no patient (black), and MPS for all patients (dashed grey). CARPE_Coll. is not shown as it is visually indistinguishable from CARPE_ECG. Net benefit puts both benefit and harm on the same scale. In our case, we consider harm to be inflicted by performing an unnecessary MPS. At a decision threshold of 5%, all approaches lead to a similar net benefit. At the second threshold of 15%, CARPE_Clin. and the cardiologist demonstrate a net benefit similar to performing MPS on all patients, with CARPE_ECG leading to a higher net benefit. Second row: Potential MPSs avoided compared to the cardiologist’s strategy: While the conventional ML model and deep learning avoid the approximately same number of MPSs at the decision threshold of 5% (11.5% and 12.8%, respectively), the gap increases at the pre-MPS threshold of 15% (15.3% and 5.3%, respectively). Envelopes in both rows show 95% confidence intervals around the mean over n = 25 bootstrap draws. Source data are provided as a Source Data file.