Deep Learning Networks Accurately Detect ST-Segment Elevation Myocardial Infarction and Culprit Vessel

Frontiers in Cardiovascular Medicine

Guifang Huang¹  Lanqing Han¹  Lu Liu²  Yong Liu³  Jianrui Zheng³  Yesheng Ling³  Rui Pu³  Bin Zhou³  Dinghui Liu³  Xiaoxian Qian³  Xianguan Yu³  Suzhen Liang³  Lin Wu⁴  Xuemin He⁴  Yanming Chen⁴  Minzhong Ye⁵  Xiangyu Liu⁶ 

[1] Center for Artificial Intelligence, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China;Department of Anesthesiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China;Department of Cardiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China;Department of Endocrine and Metabolic Diseases, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China;Novelty-Checking Center, Guangdong Institute of Scientific and Technical Information, Guangzhou, China;School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China;

关键词: ST-segment elevation myocardial infarction (STEMI);    electrocardiogram (ECG);    convolutional neural network (CNN);    long short-term memory (LSTM);    CNN-LSTM;    deep learning (DL);   

DOI  :  10.3389/fcvm.2022.797207

来源: DOAJ

【 摘 要 】

Early diagnosis of acute ST-segment elevation myocardial infarction (STEMI) and early determination of the culprit vessel are associated with a better clinical outcome. We developed three deep learning (DL) models for detecting STEMIs and culprit vessels based on 12-lead electrocardiography (ECG) and compared them with conclusions of experienced doctors, including cardiologists, emergency physicians, and internists. After screening the coronary angiography (CAG) results, 883 cases (506 control and 377 STEMI) from internal and external datasets were enrolled for testing DL models. Convolutional neural network-long short-term memory (CNN-LSTM) (AUC: 0.99) performed better than CNN, LSTM, and doctors in detecting STEMI. Deep learning models (AUC: 0.96) performed similarly to experienced cardiologists and emergency physicians in discriminating the left anterior descending (LAD) artery. Regarding distinguishing RCA from LCX, DL models were comparable to doctors (AUC: 0.81). In summary, we developed ECG-based DL diagnosis systems to detect STEMI and predict culprit vessel occlusion, thus enhancing the accuracy and effectiveness of STEMI diagnosis.

【 授权许可】

Unknown