AUTHOR=Wu Lin , Huang Guifang , Yu Xianguan , Ye Minzhong , Liu Lu , Ling Yesheng , Liu Xiangyu , Liu Dinghui , Zhou Bin , Liu Yong , Zheng Jianrui , Liang Suzhen , Pu Rui , He Xuemin , Chen Yanming , Han Lanqing , Qian Xiaoxian TITLE=Deep Learning Networks Accurately Detect ST-Segment Elevation Myocardial Infarction and Culprit Vessel JOURNAL=Frontiers in Cardiovascular Medicine VOLUME=9 YEAR=2022 URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.797207 DOI=10.3389/fcvm.2022.797207 ISSN=2297-055X ABSTRACT=

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.