Emergency cardiology gets a lift from AI-read ECGs, with fewer false activations
Researchers report AI ECG triage detected more heart attacks while slashing unnecessary activations across three PCI centres.
AI ECG analysis improved heart attack detection and reduced false alarms in a multicentre study of 1,032 suspected STEMI cases. Conducted across three primary PCI centres from January 2020 to May 2024, it points to quicker, more accurate triage, especially beyond specialist hospitals.
ST-segment elevation myocardial infarction occurs when a major coronary artery is blocked. Guideline targets call for reperfusion within 90 minutes of first medical contact. Longer delays are associated with roughly a 3-fold increase in mortality, underscoring the need for rapid, reliable activation.
The AI ECG model, trained to detect acute coronary occlusion and STEMI equivalents, analysed each patient’s initial tracing. Confirmatory angiography and biomarkers identified 601 true STEMIs and 431 false positives. AI detected 553 of 601 STEMIs, versus 427 identified by standard triage on the first ECG.
False positives fell sharply with AI. Investigators reported a 7.9 percent false-positive rate with the model, compared with 41.8 percent under standard protocols. Clinicians said earlier that more precise identification could streamline transfers from non-PCI centres and help teams reach reperfusion targets.
An editorial welcomed the gains but urged caution. The model targets acute occlusion rather than STEMI, needs prospective validation in diverse populations, and must be integrated with clear governance and human oversight.
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