Novel artificial intelligence model using electrocardiogram for detecting acute myocardial infarction needing revascularization

Aims: Rapid myocardial revascularization in patients with acute myocardial infarction (AMI) is essential to improve clinical outcomes. There is still room for improvement in the timely diagnosis of AMI. This study aimed to develop an artificial intelligence (AI) model using electrocardiograms (ECGs) for detecting AMI needing revascularization. Methods and results: A total of 723 389 ECGs from 300 627 patients in the derivation cohort at a single centre between 2013 and 2020, including 5872 patients with AMI (1.95%) who underwent revascularization, were used for model training and internal testing. A transformer-based deep learning model, initially trained on about one million unlabelled ECGs through self-supervised learning, was fine-tuned for AMI detection. The model's final performance was evaluated in the internal test and the external validation set. The external validation was conducted at an independent centre between 2002 and 2020 using 261 429 ECGs from 259 454 patients, including 1095 patients with AMI (0.42%). By integrating self-supervised learning to train the AI model, we enhanced the AMI detection performance, as demonstrated by an increase in the area under the receiver operating characteristic curve (AUROC) from 0.910 (95% CI, 0.904-0.915) to 0.968 (95% CI, 0.965-0.971) in the external validation set. For ST-elevation myocardial infarction and non-ST-elevation myocardial infarction detection, the AUROCs were 0.991 (95% CI, 0.989-0.993) and 0.947 (95% CI, 0.942-0.952) in the external validation set, respectively. Conclusion: This novel ECG-based AI model may be beneficial for the timely identification of patients with AMI needing revascularization.