Artificial Intelligence Detection of Occlusive Myocardial Infarction from Electrocardiograms Interpreted as “Normal” by the Conventional Algorithm

Background: Some authors advocate that ECGs with conventional computer algorithm (CCA) interpretations of “normal” need not be immediately reviewed. However, such ECGs may actually manifest findings of acute coronary occlusion myocardial infarction (OMI). We sought to determine if such cases can be detected by artificial intelligence (AI). Methods: Retrospective series (2014 - 2024) of cases with ≥ 1 pre-angiography ECGs with proven OMI outcome with a CCA ECG interpretation of “normal.” OMI outcome was defined as 1) diagnosis of acute type I MI, 2) angiographic culprit with intervention, 3) one of the following: a) TIMI 0-2 flow, or b) TIMI-3 or unknown flow, with high peak troponin or new wall abnormality. Each ECG was retrospectively interpreted by the PMcardio OMI AI ECG model. The primary analysis was the diagnostic performance of AI for the interpretation of CCA “normal” ECGs for OMI. Results: 42 patients with OMI met inclusion criteria. The first ECG was interpreted as “normal” by the CCA in 88% of cases; AI interpreted 81% as OMI and 86% as abnormal. Of 78 total ECGs interpreted by the CCA, 73% were diagnosed as “normal.” Of this 73%, AI identified 81% as abnormal and 72% as OMI. Conclusion: The CCA may interpret an ECG manifesting OMI as “normal.” AI not only recognized these as abnormal, but, in 81% of patients, correctly recognized OMI on the first ECG, and recognized 72% of all CCA “normal” ECGs as OMI. It was rare for AI to diagnose a normal ECG in any OMI patient.