Key result
An artificial intelligence-enabled convolutional neural network model using standard 12-lead ECGs detected significant coronary artery disease with an area under the ROC curve of 0.869.
Why the study?
The role of artificial intelligence with electrocardiograms for diagnosing significant CAD and determining the obstructed vessel was unknown.
Does an artificial intelligence-enabled CNN model applied to standard 12-lead ECGs accurately detect and localize significant coronary artery disease?
Cohort (n=3,356)
Yes
Does an artificial intelligence-enabled CNN model applied to standard 12-lead ECGs accurately detect and localize significant coronary artery disease?
Effect estimate: AUC 0.869
An AI-enabled CNN model applied to standard 12-lead ECGs can accurately detect and localize significant coronary artery disease, offering a potential non-invasive screening tool.
AI-ECG may identify significant CAD and culprit vessel; hypothesis-generating and requires prospective validation.
(1) Background: The role of using artificial intelligence (AI) with electrocardiograms (ECGs) for the diagnosis of significant coronary artery disease (CAD) is unknown. We first tested the hypothesis that using AI to read ECG could identify significant CAD and determine which vessel was obstructed. (2) Methods: We collected ECG data from a multi-center retrospective cohort with patients of significant CAD documented by invasive coronary angiography and control patients in Taiwan from 1 January 2018 to 31 December 2020. (3) Results: We trained convolutional neural networks (CNN) models to identify patients with significant CAD (>70% stenosis), using the 12,954 ECG from 2303 patients with CAD and 2090 ECG from 1053 patients without CAD. The Marco-average area under the ROC curve (AUC) for detecting CAD was 0.869 for image input CNN model. For detecting individual coronary artery obstruction, the AUC was 0.885 for left anterior descending artery, 0.776 for right coronary artery, and 0.816 for left circumflex artery obstruction, and 1.0 for no coronary artery obstruction. Marco-average AUC increased up to 0.973 if ECG had features of myocardial ischemia. (4) Conclusions: We for the first time show that using the AI-enhanced CNN model to read standard 12-lead ECG permits ECG to serve as a powerful screening tool to identify significant CAD and localize the coronary obstruction. It could be easily implemented in health check-ups with asymptomatic patients and identifying high-risk patients for future coronary events.
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Huang et al. (2022) conducted a cohort in Coronary artery disease (n=3,356). Artificial intelligence-enabled ECG algorithm vs. Control patients without CAD was evaluated on Detection of significant CAD (>70% stenosis) (AUC 0.869). An artificial intelligence-enabled convolutional neural network model using standard 12-lead ECGs detected significant coronary artery disease with an area under the ROC curve of 0.869.
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