Advancing Cardiovascular Risk Assessment with Artificial Intelligence: Opportunities and Implications in North...

Area of Science:

• Cardiology
• Artificial Intelligence
• Public Health

Background:

• Cardiovascular disease (CVD) mortality is increasing in North Carolina.
• Inequalities in CVD mortality persist across racial, income, and geographic lines.
• Electrocardiograms (ECGs) are widely available but underutilized for comprehensive cardiac assessment.

Purpose of the Study:

• To explore the potential of artificial intelligence (AI) in repurposing ECG data for enhanced cardiac dysfunction assessment.
• To investigate AI's ability to identify accelerated cardiac aging from ECGs.
• To determine if AI-driven ECG analysis can offer novel insights into CVD risk assessment and prevention.

Main Methods:

• Utilizing artificial intelligence algorithms to analyze electrocardiogram (ECG) data.
• Developing and validating AI models to detect patterns indicative of accelerated cardiac aging.
• Correlating AI-identified cardiac aging markers with existing cardiovascular risk factors and outcomes.

Main Results:

• AI successfully identified accelerated cardiac aging from standard ECGs.
• AI-based cardiac aging assessment showed potential for improved risk stratification.
• Novel insights into the mechanisms underlying CVD disparities may be revealed.

Conclusions:

• Artificial intelligence offers a promising, non-invasive method to enhance cardiovascular risk assessment using ECGs.
• AI-driven analysis of ECGs for accelerated cardiac aging could help address disparities in cardiovascular disease mortality.
• Further research is warranted to integrate AI-ECG analysis into clinical practice for cardiovascular disease prevention.