Computational Analysis of Intravascular OCT Images for Future Clinical Support: A Comprehensive Review

Area of Science:

• Cardiovascular Imaging
• Medical Artificial Intelligence
• Interventional Cardiology

Background:

• Intravascular optical coherence tomography (IVOCT) offers high-resolution imaging for coronary artery disease assessment.
• Automated analysis using artificial intelligence (AI) is increasingly applied to IVOCT data.
• AI in IVOCT holds potential for improved clinical decision-making and research insights.

Purpose of the Study:

• To review advancements in AI and computational simulation for IVOCT image analysis.
• To highlight clinical applications of AI-enhanced IVOCT in percutaneous coronary interventions (PCI).
• To discuss the potential of AI-driven IVOCT for personalized patient care.

Main Methods:

• Review of recent literature on AI techniques applied to IVOCT image analysis.
• Focus on methods for vessel wall segmentation, plaque characterization, and stent analysis.
• Exploration of computational simulation methods in conjunction with AI.

Main Results:

• AI enables fast and accurate automated interpretation of IVOCT images.
• AI facilitates comprehensive plaque assessments crucial for PCI guidance.
• AI-driven analysis aids in understanding coronary atherosclerosis pathophysiology.

Conclusions:

• AI significantly enhances IVOCT analysis for both clinical practice and research.
• AI-powered IVOCT can lead to more informed treatment decisions during PCI.
• The integration of AI in IVOCT promises improved patient outcomes through personalized medicine.