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Artificial Intelligence in Coronary Plaque Characterization: Clinical Implications, Evidence Gaps, and Future
Juthipong Benjanuwattra1, Cristian Castillo-Rodriguez2, Mahmoud Abdelnabi3
1Division of Cardiology, University of Cincinnati Medical Center, Cincinnati, OH 45219, USA.
Insights
Artificial intelligence (AI) enhances coronary artery disease (CAD) plaque analysis for better risk assessment. AI models improve detection, segmentation, and quantification, aiding personalized cardiovascular care despite adoption challenges.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Coronary artery disease (CAD) is a leading cause of death, with plaque characteristics critical for outcomes.
- Current plaque analysis methods suffer from variability and inefficiency.
- Artificial intelligence (AI) offers automated solutions for coronary plaque assessment.
Purpose of the Study:
- To review the role of AI in automated coronary plaque analysis.
- To highlight AI's potential in improving CAD risk stratification and personalized management.
- To identify barriers hindering AI adoption in cardiovascular care.
Main Methods:
- Review of AI applications in coronary plaque detection, segmentation, quantification, and vulnerability assessment.
- Analysis of AI-derived imaging biomarkers for predicting major adverse cardiovascular events.
- Discussion of challenges to AI implementation in clinical workflows.
Main Results:
- AI models demonstrate high accuracy in plaque analysis across various imaging modalities.
- AI integration with clinical scores enhances cardiovascular event prediction.
- AI-enhanced imaging shows promise for both invasive and non-invasive CAD evaluation.
Conclusions:
- AI is a transformative tool for coronary plaque analysis, improving diagnostic accuracy and patient management.
- Overcoming challenges like data heterogeneity, bias, and regulatory hurdles is crucial for widespread AI adoption.
- AI-enhanced imaging is poised to become a vital adjunct in routine cardiovascular care.
Abstract:
Coronary artery disease (CAD) remains the leading cause of cardiovascular morbidity and mortality worldwide, with plaque composition and morphology being as key determinants of disease progression and clinical outcomes. Accurate plaque characterization is essential for risk stratification and therapeutic decision-making, yet conventional image interpretation is limited by inter-observer variability and time-intensive workflows. Artificial intelligence (AI) models have emerged as a transformative tool for automated coronary plaque analysis across multiple imaging modalities. AI-driven models demonstrate high diagnostic accuracy for plaque detection, segmentation, quantification, and vulnerability assessment. Integration of AI-derived imaging biomarkers with clinical risk scores can further enhance prediction of major adverse cardiovascular events and supports personalized management. These advances position AI-enhanced imaging as a powerful adjunct for both invasive and non-invasive evaluation of CAD. Despite its promise, important barriers to widespread clinical adoption remain, including data heterogeneity, algorithmic bias, limited model transparency, insufficient prospective validation, regulatory challenges, and incomplete integration into clinical workflows. Addressing these challenges will be essential to ensure safe, generalizable, and cost-effective implementation of AI in routine cardiovascular care.
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