Human vs Artificial Intelligence in Detecting Coronary Occlusions in ST-Elevation Myocardial Infarction:...

Occlusion myocardial infarction (OMI) on the electrocardiogram (ECG) can present as ST-elevation myocardial infarction (STEMI), non-STEMI, ST depression, or other patterns (eg, de Winter T waves and hyperacute T waves), posing a diagnostic challenge. Even experienced physicians may overlook OMI on ECG, leading to delays in catheterization laboratory activation. Recently, smartphone apps using AI have been shown to exhibit promising accuracy in ECG interpretation in large datasets.

The objective of our study was to retrospectively compare the diagnostic performance of an AI-based application (PMCardio) with that of interventional cardiologists in interpreting nonpathognomonic ECGs that generated debates about diagnosis.

In total, 33 ECGs that were diagnostically challenging upon patient admission to the emergency department were included. A group of 3 interventional cardiologists, blinded to the final diagnosis, independently assessed whether the ECG suggested OMI requiring immediate invasive coronary angiography. The diagnostic performance of the cardiologists was compared to that of the AI application, using the results of coronary angiography as the gold standard and considering any lesion with ≥90% stenosis as positive.

Of the 33 patients, 29 (88%) had a final diagnosis of cardiac origin for their chest pain, including 23 acute coronary syndromes, of which 13 (57%) were OMI. The 3 cardiologists achieved sensitivities of 62%, 85%, and 62%; negative predictive values (NPVs) of 74%, 83%, and 69%; specificities of 70%, 50%, and 55%; and positive predictive values (PPVs) of 57%, 52%, and 47%. Their overall misclassification rates were 33%, 36%, and 42%. In contrast, AI analysis demonstrated a sensitivity and NPV of 100%, a specificity of 60%, a PPV of 62%, and an overall misclassification rate of 24%.

In this retrospective study of challenging ECGs, an AI application demonstrated better diagnostic performance of OMI as compared to cardiologists. These findings highlight the potential of AI to enhance diagnostic accuracy and support clinical decision-making.