J Cardiovasc Interv.  2023 Apr;2(2):100-112. 10.54912/jci.2022.0028.

Usefulness of Deep-Learning Algorithm for Detecting Acute Myocardial Infarction Using Electrocardiogram Alone in Patients With Chest Pain at Emergency Department: DAMI-ECG Study

• Lee BT
• Kwon Jm
• Cho J
• Bae W
• Park H
• Seo WW
• Cho I
• Lee Y
• Park J
• Oh BH
• Jeon KH

• KMID: 2561266
• DOI: http://doi.org/10.54912/jci.2022.0028

Abstract

Background
Electrocardiogram (ECG) is the first-line modality for identifying acute myocardial infarction (AMI) in patients with chest pain. However, the ECG can even be normal in AMI patients, thereby delaying diagnosis and adversely affecting prognosis. We aim to develop a deep-learning algorithm for detecting AMI using 12-lead ECG (DAMI-ECG).
Methods
This study included retrospective cohorts from 2 separate hospitals. We developed and validated DAMI-ECG and estimated the diagnostic performance in patients who visited the emergency department (ED) for chest pain. Furthermore, we compared the accuracy of DAMI-ECG through interpretations of an ECG machine and experienced cardiologists.
Results
A total of 227,912 ECGs from 114,600 patients were used to develop and validate DAMI-ECG, and 2,274 ECGs from 1,765 patients who visited the ED for chest pain were used to estimate the diagnostic performance in a clinical setting. Among development and validation datasets, the area under the receiver operating characteristic curve of the DAMI-ECG for detecting AMI was 0.927 (95% confidence interval, 0.908–0.945) and 0.914 (0.899–0.929) during internal and external validations. The diagnostic accuracy of DAMIECG for detecting AMI in patients with chest pain at the ED was 86.1% (83.6–88.6%), which outperformed that of experienced cardiologists (78.6% [76.8–80.1%]) using ECG alone.
Conclusion
In conclusion, the diagnostic performance of DAMI-ECG is excellent in detecting AMI in patients visiting the ED for chest pain and superior to that of cardiologists. This algorithm can be used in the ED or pre-hospital setting for early AMI diagnosis.

Keyword

Myocardial infarction; Artificial intelligence; Deep learning; Electrocardiography