AI model outperforms standard heart attack tests, could speed emergency care
Researchers have developed an artificial intelligence system that detects dangerous heart attacks from ECG readings with 95% accuracy—significantly better than current clinical standards that rely on ST-segment changes and troponin tests. The breakthrough could reduce delays in getting patients to life-saving procedures and cut unnecessary emergency catheterizations, reshaping how hospitals triage cardiac emergencies.
Originaltitel: A deep learning ECG model for identification and localization of occlusion myocardial infarction
Abstract 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 present a trained and validated deep learning model using 540,372 emergency ECGs paired with definitive catheterization outcomes. The model has a C-statistic of ≥0.95 for OMI and ≥0.87 for non-OMI infarctions and can localize culprit lesions in the three main coronary branches, which can guide the angiographer. Performance is similar across age, sex, and ECG hardware subgroups. Obviating dependence on ST-elevations and troponins, this model for the identification and localization of OMI has the potential to shorten the 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.