Development of Clinically Validated Artificial Intelligence Model for Detecting ST-segment Elevation Myocardial Infarction

Abstract

ObjectiveAlthough the importance of primary percutaneous coronary intervention (PCI) has been emphasized for ST-segment elevation myocardial infarction (STEMI), the appropriateness of the cardiac catheterization laboratory (CCL) activation remains suboptimal. This study aimed to develop a precise artificial intelligence (AI) model for diagnosis of STEMI and accurate CCL activation. MethodsWe used electrocardiography (ECG) waveform data from a prospective PCI registry in Korea in this study. Two independent board-certified cardiologists established a criterion standard (STEMI or Not-STEMI) for each ECG based on corresponding coronary angiography data. We developed a deep ensemble model by combining five convolutional neural networks. In addition, we performed clinical validation based on a symptom-based ECG dataset, comparisons with clinical physicians, and external validation. ResultsWe used 18,697 ECGs for the model development dataset and 1,745 (9.3%) were STEMI. The AI model achieved an accuracy of 92.1%, sensitivity of 95.4% and specificity of 91.8 %. The performances of the AI model were well-balanced and outstanding in the clinical validation, comparison with clinical physicians, and the external validation. ConclusionsThe deep ensemble AI model showed a well-balanced and outstanding performance. As visualized with the Grad-CAM, the AI model has a reasonable explainability. Further studies with prospective validation regarding clinical benefit in a real-world setting should be warranted.