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.
Highlights
ST-segment elevation myocardial infarction (STEMI) is one of the leading cardiovascular diseases, with a high morbidity and mortality [1]
To avoid the shortcomings of previous deep learning (DL) models, we aimed to develop highly effective and accurate DL models for diagnosing STEMI and culprit vessels
A total of 1,259 individuals performed coronary angiography (CAG) were collected in the Third Affiliated Hospital of Sun Yat-sen University (Figure 1A)
Summary
ST-segment elevation myocardial infarction (STEMI) is one of the leading cardiovascular diseases, with a high morbidity and mortality [1]. ST-segment elevation is considered to reflect acute transmural ischemia caused by epicardial coronary artery blockage. The timely diagnosis of STEMI is crucial to guide therapy and lower sudden cardiac death [2]. Coronary angiography (CAG) is the gold standard for diagnosing STEMI. CAG is an invasive, inconvenient, expensive, and radioactive examination and requires a monitoring infrastructure. An effective technique for screening STEMI patients is urgently required to distinguish STEMI from other diseases
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