Artificial Intelligence-based Approaches for Characterizing Plaque Components From Intravascular Optical Coherence...

Area of Science:

• Cardiovascular Imaging
• Artificial Intelligence in Medicine
• Medical Image Analysis

Background:

• Intravascular optical coherence tomography (IVOCT) offers detailed coronary plaque characterization but faces limited clinical adoption due to manual assessment challenges.
• Manual plaque analysis is time-consuming, error-prone, and suffers from high inter-observer variability, hindering its routine clinical application.
• Artificial intelligence (AI) is being integrated to automate and improve the precision of IVOCT image analysis for coronary artery disease.

Purpose of the Study:

• To provide a comprehensive overview of AI-based methods for analyzing IVOCT images of coronary arteries, focusing on plaque characterization.
• To explore the clinical translation of AI in IVOCT, highlighting current AI-powered tools for plaque characterization.
• To identify limitations and future directions for AI in IVOCT for enhanced clinical decision-making.

Main Methods:

• Review of AI-based techniques, including machine learning and deep learning (e.g., convolutional neural networks), applied to IVOCT image analysis.
• Focus on automatic feature extraction and classification of coronary atherosclerotic plaques.
• Exploration of commercially available or clinically intended AI-powered IVOCT analysis tools.

Main Results:

• AI, particularly deep learning, demonstrates robust performance in classifying plaque types and automating feature extraction from IVOCT images.
• Several AI-driven tools are emerging for plaque characterization, aiming to improve efficiency and reproducibility.
• Current AI solutions have limitations in the scope of assessable plaque features and are often restricted to specific regulatory or research settings.

Conclusions:

• AI integration holds significant potential to transform IVOCT from a research tool into a clinical decision-making aid for coronary artery disease.
• Further advancements, validation, and seamless integration with clinical systems are crucial for widespread adoption of AI-powered IVOCT analysis.
• Enhanced AI-based IVOCT analysis can improve plaque characterization, support clinical decision-making, and advance cardiovascular risk prediction.