International evaluation of an artificial intelligence-powered ecg model detecting occlusion myocardial infarction

Background One third of Non-ST-elevation myocardial infarction (NSTEMI) patients present with an acutely occluded culprit coronary artery (occlusion myocardial infarction [OMI]), which is associated with poor short and long-term outcomes due to delayed identification and consequent delayed invasive management. We sought to develop and validate a versatile artificial intelligence (AI)-model detecting OMI on single standard 12-lead electrocardiograms (ECGs) and compare its performance to existing state-of-the-art diagnostic criteria. Methods An AI model was developed using 18,616 ECGs from 10,692 unique contacts (22.9% OMI) of 10,543 patients (age 66±14 years, 65.9% males) with acute coronary syndrome (ACS) originating from an international online database and a tertiary care center. This AI model was tested on an international test set of 3,254 ECGs from 2,263 unique contacts (20% OMI) of 2,222 patients (age 62±14 years, 67% males) and compared with STEMI criteria and annotations of ECG experts in detecting OMI on 12-lead ECGs using sensitivity, specificity, predictive values and time to OMI diagnosis. OMI was based on a combination of angiographic and biomarker outcomes. Results The AI model achieved an area under the curve (AUC) of 0.941 (95% CI: 0.926-0.954) in identifying the primary outcome of OMI, with superior performance (accuracy 90.7% [95% CI: 89.5-91.9], sensitivity 82.6% [95% CI: 78.9-86.1], specificity 92.8 [95% CI: 91.5-93.9]) compared to STEMI criteria (accuracy 84.9% [95% CI: 83.5-86.3], sensitivity 34.4% [95% CI: 30.0-38.8], specificity 97.6% [95% CI: 96.8-98.2]) and similar performance compared to ECG experts (accuracy 91.2% [95% CI: 90.0-92.4], sensitivity 75.9% [95% CI: 71.9-80.0], specificity 95.0 [95% CI: 94.0-96.0]). The average time from presentation to a correct diagnosis of OMI was significantly shorter when relying on the AI model compared to STEMI criteria (2.0 vs. 4.9 hours, p<0.001). Conclusions The present novel ECG AI model demonstrates superior accuracy and earlier diagnosis of AI to detect acute OMI when compared to the STEMI criteria. Its external and international validation suggests its potential to improve ACS patient triage with timely referral for immediate revascularization. What is new? What are the clinical implications? ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Dr. Herman is the Co-founder and Chief Medical Officer of Powerful Medical; Michal Martonak, Jakub Bahyl, Andrej Iring, Boris Vavrik, Vladimir Boza, Viera Kresnakova and Anthony Demolder are employees and shareholders of Powerful Medical. Dr. Smith, Dr. Meyers and Dr. Perl are shareholders in Powerful Medical. Dr. Herman, Dr. Bertolone, Dr. Leone, Dr. Viscusi are supported by a research grant from the CardioPaTh PhD Program. Other authors report no conflict of interest. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Not Applicable The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Cardiovascular Center Aalst I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Not Applicable I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as [ClinicalTrials.gov][1]. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Not Applicable I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Not Applicable The OMI AI ECG model is available for external validation, benchmarking and research use at: . The full dataset is not available for public sharing, given our institutional review board approval restrictions. . [1]: http://ClinicalTrials.gov