Related Experiment Video
Updated: Jul 30, 2025
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
Published on: April 11, 2025
The feasibility of early detecting coronary artery disease using deep learning-based algorithm based on
Panli Tang1, Qi Wang1, Hua Ouyang1
1Department of Cardiovascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China.
Insights
Artificial intelligence can now detect Coronary Artery Disease (CAD) using only electrocardiograms (ECG). This noninvasive approach aids in early CAD detection, improving patient outcomes.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging Analysis
Background:
- Coronary Artery Disease (CAD) is a leading cause of death and illness.
- Early-stage CAD is often asymptomatic, leading to delayed diagnosis.
- Current diagnostic methods can be invasive or costly.
Purpose of the Study:
- To develop an artificial intelligence (AI) model for early Coronary Artery Disease (CAD) detection.
- To utilize electrocardiogram (ECG) data exclusively for CAD patient identification.
- To establish a noninvasive and cost-effective screening tool.
Main Methods:
- A convolutional neural network (CNN) model was developed using patient ECG and coronary computed tomography angiography (cCTA) data.
- Data was randomly allocated into training, validation, and testing datasets.
- Model performance was evaluated using accuracy, sensitivity, specificity, PPV, NPV, and AUC.
Main Results:
- The AI model achieved an Area Under the Curve (AUC) of 0.75 for CAD detection.
- The model demonstrated 70.0% accuracy, 68.7% sensitivity, and 70.9% specificity.
- Positive predictive value was 61.2% and negative predictive value was 77.2%.
Conclusions:
- A CNN model trained on ECG data can effectively assist in early Coronary Artery Disease detection.
- This AI-driven ECG analysis offers an efficient, low-cost, and noninvasive diagnostic aid.
- The findings support the use of AI-powered ECG for broader CAD screening.
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.
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