Research Paper Volume 15, Issue 9 pp 3524—3537

The feasibility of early detecting coronary artery disease using deep learning-based algorithm based on electrocardiography

Panli Tang1, *, , Qi Wang1, *, , Hua Ouyang1, , Songran Yang2, , Ping Hua1, ,

  • 1 Department of Cardiovascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
  • 2 The Biobank of Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
* Equal contribution

Received: March 18, 2022       Accepted: April 13, 2023       Published: May 1, 2023
How to Cite

Copyright: © 2023 Tang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Background: Coronary Artery Disease (CAD) is a major cause of morbidity and mortality, yet it is frequently asymptomatic in the early stages and hence goes undetected.

Objective: We aimed to develop a novel artificial intelligence-based approach for early detection of CAD patients based solely on electrocardiogram (ECG).

Methods: This study included patients with suspected CAD who had standard 10-s resting 12-lead ECGs and coronary computed tomography angiography (cCTA) results within 4 weeks or less. The ECG and cCTA data from the same patient were matched based on their hospitalization or outpatient ID. All matched data pairs were then randomly divided into training, validation dataset for model development based on convolutional neural network (CNN) and test dataset for model evaluation. The accuracy (Acc), specificity (Spec), sensitivity (Sen), positive predictive value (PPV), negative predictive value (NPV) and area under the receiver operating characteristic curve (AUC) of the model were calculated by using the test dataset.

Results: In the test dataset, the model for detecting CAD achieved an AUC of 0.75 (95% CI, 0.73 to 0.78) with an accuracy of 70.0%. Using the optimal cut-off point, the CAD detection model had sensitivity of 68.7%, specificity of 70.9%, positive predictive value (PPV) of 61.2%, and negative predictive value (NPV) of 77.2%. Our study demonstrates that a well-trained CNN model based solely on ECG could be considered an efficient, low-cost, and noninvasive method of assisting in CAD detection.


AI: artificial intelligence; ECG: electrocardiography; DL: deep learning; CAD: coronary artery disease; cCTA: coronary computed tomography angiography; CAG: coronary angiography; CNN: convolutional neural network; Acc: accuracy; Spec: specificity; Sen: sensitivity; PPV: positive predictive value; NPV: negative predictive value; AUC: area under the receiver operating characteristic curve; CVDs: cardiovascular diseases; CABG: coronary artery bypass grafting; PCI: prior percutaneous coronary intervention; ROC: receiver operating curve.