Artificial intelligence (AI)-based ECG interpretation outperformed standard pathways for the detection of occlusive myocardial infarction (MI), according to a study presented at ESC Acute CardioVascular Care 2026, the annual congress of the Association for Acute Cardio Vascular Care (ACVC), a branch of the European Society of Cardiology (ESC).

In patients with suspected acute coronary syndrome (ACS), a specific change on an ECG, called an ST elevation, is an indicator that the patient may have an occlusion in a coronary artery. This type of heart attack is known as an ST-elevation myocardial infarction (STEMI) and it requires immediate percutaneous coronary intervention to restore the heart's blood flow.

In patients who do not have an ST elevation, the cause of the chest pain can be less certain and further tests are needed to confirm if the MI is due to an occlusion.

Presenter, Doctor Federico Nani from Central Hospital Bolzano, Italy, explained, "Many patients without an ST elevation have an occlusive MI, but it can be difficult for clinicians to quickly and accurately recognize this, leading to delays in providing emergency treatment. We investigated whether AI-based interpretation of the initial ECG could improve the accuracy of detecting occlusive MIs in the absence of an ST elevation to optimize patient management."

This single-center prospective study included 1,490 patients who had symptoms suggestive of ACS but without an ST elevation on the initial ECG. The mean age was 63 years and 42% were female.

Clinicians interpreted the initial ECG, tested levels of the cardiac biomarker troponin, and performed coronary angiography, when required, to diagnose occlusive MI based on ESC Guidelines. In parallel, the initial ECG was interpreted by a smartphone-based CE-certified AI-ECG algorithm.