AI outperforms conventional diagnosis for certain types of heart attacks: Study | Image:
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An artificial intelligence (AI)-based method for interpreting ECGs performed better than standard approaches in detecting occlusive myocardial infarction (MI), according to a study presented at ESC Acute CardioVascular Care 2026, the annual congress of the Association for Acute CardioVascular 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 recognise 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 optimise patient management.”
This single-centre 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 pe
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