A deep learning ECG model for localization of occlusion myocardial infarction

Rapid identification and localization of an acute coronary occlusion are vital to prevent myocardial damage, yet reliance on ST-segment ECG criteria misses many acute occlusion myocardial infarctions (OMI) and triggers unnecessary acute angiographies. Here, we trained and validated a deep learning model using 540,372 emergency ECGs paired with definitive catheterization outcomes. The model achieved a C-statistic of >0.95 for OMI and >0.87 for non-OMI infarctions and could localize culprit lesions in the left main/LAD, LCX, and RCA coronary branches, which can guide the angiographer. Performance was similar across age, sex, and ECG hardware subgroups. Obviating dependence on ST-elevations and troponins, this model for identification and localization of OMI has the potential to shorten time to reperfusion of an acute coronary occlusion and save resources. Because human oversight of OMI detection on the ECG is limited, randomized clinical trials with patient-relevant outcomes are warranted.