Artificial Intelligence in Coronary Plaque Characterization: Clinical Implications, Evidence Gaps, and Future...

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.