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Updated: May 30, 2025
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
Published on: April 11, 2025
Artificial intelligence-enhanced electrocardiography improves the detection of coronary artery disease
Chi-Hsiao Yeh1,2,3, Tsung-Hsien Tsai4, Chun-Hung Chen4
1Department of Thoracic and Cardiovascular Surgery, Chang Gung Memorial Hospital, Linkou, Taoyuan 333, Taiwan.
Insights
An AI algorithm enhances electrocardiogram (ECG) analysis to detect coronary artery disease (CAD) in high-risk patients with normal ECGs. This AI tool offers a cost-effective and accessible alternative to traditional diagnostic methods.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Coronary artery disease (CAD) detection can be challenging in patients with normal electrocardiograms (ECGs).
- Current diagnostic methods may be invasive, costly, or inaccessible for all patient populations.
Purpose of the Study:
- To develop and validate an AI-assisted algorithm for improved detection of significant CAD using 12-lead ECGs.
- To assess the performance of the AI algorithm in patients with both normal and abnormal ECGs, including those with and without ischemia.
- To compare the AI-enhanced ECG's diagnostic capability with existing methods like myocardial perfusion scintigraphy.
Main Methods:
- Retrospective analysis of 12-lead ECG datasets from adult patients undergoing coronary angiography.
- Development of an AI algorithm integrating 561 ECG features (time intervals, amplitudes, slopes) using the XGBoost model.
- Evaluation of algorithm sensitivity and prediction rates for CAD detection across different ECG subgroups.
Main Results:
- The AI-enhanced ECG algorithm showed high sensitivity (82-84%) for detecting CAD in patients with normal ECGs.
- Remarkably high prediction rates were achieved for patients with abnormal ECGs (92-95% with ischemia, 80-83% without ischemia).
- The AI algorithm's performance matched that of myocardial perfusion scintigraphy, identifying key features difficult for manual clinical assessment and revealing significant sex-based differences.
Conclusions:
- AI-assisted ECG analysis offers a promising, non-invasive, and accessible tool for detecting significant CAD, particularly in individuals with normal or atypical ECG readings.
- The algorithm's ability to identify subtle ECG features surpasses manual interpretation, providing a valuable adjunct to traditional diagnostic approaches.
- This AI-enhanced ECG method presents a cost-effective alternative to nuclear imaging for CAD screening and diagnosis.
Abstract:
An AI-assisted algorithm has been developed to improve the detection of significant coronary artery disease (CAD) in high-risk individuals who have normal electrocardiograms (ECGs). This retrospective study analyzed ECGs from patients aged ≥ 18 years who were undergoing coronary angiography to obtain a clinical diagnosis at Chang Gung Memorial Hospital in Taiwan. Utilizing 12-lead ECG datasets, the algorithm integrated features like time intervals, amplitudes, and slope between peaks, a total of 561 features, with the XGBoost model yielding the best performance. The AI-enhanced ECG algorithm demonstrated high sensitivity (0.82-0.84) when detecting CAD in patients with normal ECGs and gave remarkably high prediction rates among those with abnormal ECGs, both with and without ischemia (92 %-95 % and 80 %-83 %, respectively). Notably, the algorithm's top features, mostly related to slope and amplitude differences, are challenging for clinicians to discern manually. Additionally, the study highlights significant sex differences regarding feature prediction and ranking. Comparatively, the AI-enhanced ECG's detection capability matched that of myocardial perfusion scintigraphy, which is a costly nuclear medicine test, and offers a more accessible alternative for identifying significant CAD, especially among patients with atypical ECG readings.
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