AI Applications in Electrocardiography for Ischemic and Structural Heart Disease: A Review of the Current State
Eugene J Kim1, Dhir Gala1, Mohammed Ayyad1
1Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ 07003, USA.
Insights
Artificial intelligence (AI) enhances electrocardiogram (ECG) analysis for cardiovascular disease detection. AI-powered ECGs promise earlier diagnosis and personalized treatments for heart conditions.
Area of Science:
- Cardiology
- Artificial Intelligence
- Machine Learning
- Medical Diagnostics
Background:
- Cardiovascular disease (CVD) is a major global health burden, with ischemic and structural heart conditions being primary drivers.
- The 12-lead electrocardiogram (ECG) is a widely used diagnostic tool, but its interpretation is subject to human variability.
- Automated analysis of ECGs using artificial intelligence (AI) and machine learning (ML) offers potential for improved accuracy and consistency.
Purpose of the Study:
- To explore the application of AI-augmented ECG algorithms for automated cardiovascular disease detection.
- To assess the potential of AI in identifying subtle ECG patterns indicative of various heart conditions.
- To evaluate AI's role in enhancing cardiovascular diagnostics, risk stratification, and intervention strategies.
Main Methods:
- Utilized machine learning algorithms trained on large, diverse ECG datasets.
- Developed AI models for automated pattern recognition in ECG waveforms.
- Investigated the capability of AI to detect myocardial infarction, left ventricular dysfunction, aortic stenosis, and hypertrophic cardiomyopathy.
Main Results:
- AI-augmented ECG algorithms demonstrated high accuracy in detecting myocardial infarction.
- These algorithms showed potential in reducing door-to-balloon times for coronary interventions.
- AI models can identify subtle ECG signatures associated with asymptomatic left ventricular dysfunction, aortic stenosis, and hypertrophic cardiomyopathy.
Conclusions:
- AI-augmented ECG analysis offers a powerful tool for automating and improving the accuracy of cardiovascular diagnostics.
- Potential exists for earlier detection and risk stratification of various heart conditions, including asymptomatic ones.
- Concerns regarding data generalizability, bias, and errors must be addressed as AI systems evolve towards multimodal integration for redefined cardiovascular care.
Abstract:
Cardiovascular disease is the leading cause of morbidity and mortality worldwide, with ischemic and structural heart diseases being key contributors. While the 12-lead electrocardiogram (ECG) is a common low-cost diagnostic test, its interpretation is limited by human variability. Through machine learning with large diverse ECG data sets and artificial intelligence (AI) algorithms, ECG analysis can be automated for pattern recognition with higher accuracy. AI-augmented ECG algorithms have been demonstrated to be able to detect myocardial infarction with high accuracy and reduce door-to-balloon coronary intervention times. Similar models can be utilized to detect subtle ECG waveforms suggestive of current or future asymptomatic left ventricular dysfunction, aortic stenosis, and hypertrophic cardiomyopathy. Despite these promising results, there is concern for generalizability and bias or errors in training data. As AI systems evolve to multimodal integration, AI-augmented ECG has the potential to redefine cardiovascular diagnostics and enable earlier detection, risk stratification, and precision-guided interventions.
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