AI model detected acute occlusion myocardial infarction with 90.9% accuracy, 80.6% sensitivity, and 93.7% specificity, outperforming STEMI criteria's 32.5% sensitivity.
Does an artificial intelligence model improve the detection of acute occlusion myocardial infarction on 12-lead ECGs compared to STEMI criteria and ECG experts in patients with suspected ACS?
An AI-powered ECG model demonstrated superior accuracy and sensitivity in detecting acute occlusion myocardial infarction compared to standard STEMI criteria, potentially improving triage for emergent revascularization.
Absolute Event Rate: 0% vs 0%
Abstract Background A majority of acute coronary syndromes (ACS) present without typical ST elevation. One-third of non-ST-elevation myocardial infarction (NSTEMI) patients have an acutely occluded culprit coronary artery occlusion myocardial infarction (OMI), leading to poor outcomes due to delayed identification and invasive management. Purpose In this study, we sought to develop a versatile artificial intelligence (AI) model detecting acute OMI on single-standard 12-lead electrocardiograms (ECGs) and compare its performance with existing state-of-the-art diagnostic criteria. Methods An AI model was developed using 18 616 ECGs from 10 543 patients with suspected ACS from an international database with clinically validated outcomes. The model was evaluated in an international cohort and compared with STEMI criteria and ECG experts in detecting OMI. The primary outcome of OMI was an acutely occluded or flow-limiting culprit artery requiring emergent revascularization. Results In the overall test set of 3254 ECGs from 2222 patients (age 62 ± 14 years, 67% males, 21.6% OMI), the AI model achieved an area under the curve of 0.938 95% confidence interval (CI): 0.924-0.951 in identifying the primary OMI outcome, with superior performance accuracy 90.9% (95% CI: 89.7-92.0), sensitivity 80.6% (95% CI: 76.8-84.0), and specificity 93.7 (95% CI: 92.6-94.8) compared with STEMI criteria accuracy 83.6% (95% CI: 82.1-85.1), sensitivity 32.5% (95% CI: 28.4-36.6), and specificity 97.7% (95% CI: 97.0-98.3) and with similar performance compared with ECG experts accuracy 90.8% (95% CI: 89.5-91.9), sensitivity 73.0% (95% CI: 68.7-77.0), and specificity 95.7% (95% CI: 94.7-96.6). Conclusion The present novel ECG AI model demonstrates superior accuracy to detect acute OMI when compared with STEMI criteria. This suggests its potential to improve ACS triage, ensuring appropriate and timely referral for immediate revascularization.ROC Curves on testing Dataset A real world demonstration
Policastro et al. (Sat,) reported a other. AI model detected acute occlusion myocardial infarction with 90.9% accuracy, 80.6% sensitivity, and 93.7% specificity, outperforming STEMI criteria's 32.5% sensitivity.