In one of the first large, real-world evaluations of an AI–based ECG model for STEMI triage in the emergency setting, researchers retrospectively looked at 1,032 patients with suspected STEMI who triggered emergency reperfusion protocols.
Each patient's initial ECG underwent analysis by the STEMI AI ECG Model (Queen of Hearts) trained to detect acute coronary occlusion, including STEMI equivalents and differentiate from benign mimics.
The AI ECG model did better than standard triage, detecting 553 of 601 confirmed STEMIs vs. 427 detected by standard triage on the initial ECG.
AI ECG had a false positive rate of 7.9% vs. 41.8% for standard triage, representing a fivefold reduction.
These results indicate that AI-enhanced STEMI diagnosis at the first medical contact has the potential to shorten time to treatment and reduce false activations.
This technology may be especially valuable in optimizing the transfer of STEMI patients from non-PCI centers to ensure timely and appropriate care.
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