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Published on: August 9, 2024
Improving detection of obstructive coronary artery disease with an artificial intelligence-enabled electrocardiogram
Yin-Hao Lee1, Ming-Tsung Hsieh2, Chun-Chin Chang3
1Division of Cardiology, Department of Medicine, Taipei City Hospital, Yang Ming Branch, Taipei, Taiwan; Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Insights
An artificial intelligence (AI) model using electrocardiograms (ECG) shows promise in identifying coronary artery disease (CAD). This AI tool performs comparably to traditional risk factors and surpasses cardiologists in diagnosing obstructive CAD.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Traditional coronary artery disease (CAD) risk assessment relies on symptoms, cardiovascular risk factors (CVRFs), and electrocardiograms (ECG).
- Current ECG interpretation lacks established criteria for diagnosing CAD.
- There is a need for improved diagnostic tools to identify patients with CAD.
Purpose of the Study:
- To develop and evaluate an artificial intelligence (AI)-enabled ECG model for identifying patients with obstructive CAD.
- To compare the AI model's performance against traditional CVRFs and cardiologists' interpretations.
- To validate the AI model's efficacy on an external patient cohort.
Main Methods:
- A cohort of 4951 patients undergoing coronary angiography (CAG) was analyzed.
- Stacking models using deep learning and machine learning were developed utilizing age, gender, and ECG data.
- Model performance was assessed by comparing its predictive accuracy (AUC, sensitivity, specificity, F1 score) against CVRFs and cardiologists, with external validation.
Main Results:
- The AI model demonstrated comparable performance to CVRFs in predicting CAD (AUC 0.70 vs 0.71).
- The AI model outperformed cardiologists in diagnosing obstructive CAD (F1 score 0.68 vs 0.41).
- External validation confirmed generally consistent diagnostic findings, and combining ECG with CVRFs improved predictive accuracy (AUC 0.72).
Conclusions:
- An AI-enabled ECG model can effectively assist in identifying patients with obstructive CAD.
- The AI model's diagnostic performance is similar to traditional CVRF-based approaches.
- This AI tool holds potential as a valuable clinical aid in outpatient settings for patient triage and further diagnostic testing.
Background And Aims:
To evaluate the risk of coronary artery disease (CAD), the traditional approach involves assessing the patient's symptoms, traditional cardiovascular risk factors (CVRFs), and a 12-lead electrocardiogram (ECG). However, currently, there are no established criteria for interpreting an ECG to diagnose CAD. Therefore, we sought to develop an artificial intelligence (AI)-enabled ECG model to assist in identifying patients with CAD.
Methods:
In this study, we included patients who underwent coronary angiography (CAG) at a single center between 2017 and 2019. Preprocedural 12-lead ECG performed within 24 h was obtained. Obstructive CAD was defined as ≥ 50% diameter stenosis. Using age, gender and ECG data, we developed stacking models using both deep learning and machine learning. Then we compared the performance of our models with CVRFs and with cardiologists' ECG interpretation. Additionally, we validated our model on an external cohort from a different hospital.
Results:
We included 4951 patients with a mean age of 65.5 ± 12.5 years, of whom 67.0% were men. Based on CAG, obstructive CAD was confirmed in 2637 patients (53.2%). Our best AI model demonstrated comparable performance to CVRFs in predicting CAD, with an AUC of 0.70 (95% CI: 0.66-0.75) compared to 0.71 (95% CI: 0.66-0.76). The sensitivity and specificity of the AI model were 0.75 and 0.54, respectively, while those of CVRFs were 0.67 and 0.63. Compared to cardiologists, the AI model showed better performance with an F1 score of 0.68 vs 0.41. The external validation showed generally consistent diagnostic findings, although there was a slightly lower level of agreement observed in the external cohort. Incorporating ECG and CVRFs improved the AUC to 0.72.
Conclusions:
Our study suggests that an AI-enabled ECG model can assist in identifying patients with obstructive CAD, with diagnostic performance similar to that of the traditional approach based on CVRFs. This model could serve as a useful clinical tool in an outpatient setting to identify patients who require further diagnostic tests.
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