Kim Jongkwang, Shon Byungeun, Kim Sangwook, Cho Jungrae, Seo Jung-Ju, Jang Se Yong, Jeong Sungmoon
Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
Research Center for AI in Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea.
Front Physiol. 2024 Oct 7;15:1462847. doi: 10.3389/fphys.2024.1462847. eCollection 2024.
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.
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).
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.
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.
急性冠状动脉综合征（ACS）是全球心血管疾病死亡的主要原因之一，ST段抬高型心肌梗死（STEMI）是ACS的一种严重形式，具有高发病率和死亡率。本研究提出了一种基于深度学习的人工智能（AI）算法，利用12导联心电图（ECG）数据准确诊断STEMI并详细划分梗死区域的新方法。
本研究利用一个由888例心肌梗死（MI）患者组成的ECG数据库，通过五折交叉验证提高了AI模型的泛化能力。开发的ST段抬高（STE）检测器能够准确识别所有12导联上的STE，这是STEMI临床ECG诊断的关键指标。该检测器被应用于AI模型中，以区分STEMI和非ST段抬高型心肌梗死（NSTEMI）。
在区分STEMI和NSTEMI的过程中，受试者工作特征曲线下的平均面积（AUROC）为0.939，精确召回率曲线下的面积（AUPRC）为0.977，结果显著。此外，该检测器能够准确区分ECG中的各种梗死区域，包括前壁心肌梗死（AMI）、下壁心肌梗死（IMI）、侧壁心肌梗死（LMI）和疑似左主干病变。
这些结果表明，将临床领域与AI技术整合用于ECG诊断，对STEMI患者的快速治疗和改善预后具有关键作用。本研究为心血管疾病的诊断提供了一种创新方法，有助于提高基于AI的诊断工具在临床环境中的实际适用性。
