ECG data analysis to determine ST-segment elevation myocardial infarction and infarction territory type: an integrative approach of artificial intelligence and clinical guidelines - Kim, Jongkwang; Shon, Byungeun; Kim, Sangwook; Cho, Jungrae; Seo, Jung-Ju; Jang, Se Yong; Jeong, Sungmoon (Frontiers in Physiology, 2024)
Introduction Acute coronary syndrome (ACS) is one of the leading causes of death from cardiovascular diseases worldwide, with ST-segment elevation myocardial infarction (STEMI) representing a severe form of ACS that exhibits high prevalence and mortality rates. This study proposes a new method for accurately diagnosing STEMI and categorizing the infarction area in detail, based on 12-lead electrocardiogram (ECG) data using a deep learning-based artificial intelligence (AI) algorithm. Methods Utilizing an ECG database consisting of 888 myocardial infarction (MI) patients, this study enhanced the generalization ability of the AI model through five-fold cross-validation. The developed ST-segment elevation (STE) detector accurately identified STE across all 12 leads, which is a crucial indicator for the clinical ECG diagnosis of STEMI. This detector was employed in the AI model to differentiate between STEMI and non-ST-segment elevation myocardial infarction (NSTEMI). Results In the process of distinguishing between STEMI and NSTEMI, the average area under the receiver operating characteristic curve (AUROC) was 0.939, and the area under the precision-recall curve (AUPRC) was 0.977, demonstrating significant results. Furthermore, this detector exhibited the ability to accurately differentiate between various infarction territories in the ECG, including anterior myocardial infarction (AMI), inferior myocardial infarction (IMI), lateral myocardial infarction (LMI), and suspected left main disease. Discussion These results suggest that integrating clinical domains into AI technology for ECG diagnosis can play a crucial role in the rapid treatment and improved prognosis of STEMI patients. This study provides an innovative approach for the diagnosis of cardiovascular diseases and contributes to enhancing the practical applicability of AI-based diagnostic tools in clinical settings.