Artificial Intelligence-Assisted Intravascular Imaging in Percutaneous Coronary Intervention: Current Status and Advances
TL;DR
This review aims to summarize recent research progress in AI-assisted IVI across the PCI process and discuss future directions to improve the intelligence and efficiency of intravascular imaging.
Abstract
Over the past two decades, intravascular imaging (IVI) has been widely integrated into clinical practice. Unlike traditional angiography, IVI uses an intracoronary imaging catheter to generate high-resolution cross-sectional images of diseased coronary arteries, thereby facilitating lesion characterization. These detailed images enable more accurate guidance of percutaneous coronary intervention (PCI). The clinical utility of IVI is demonstrated across four key stages: preoperative lesion assessment, intraoperative procedural guidance, postoperative complication detection, and evaluation of stent optimization. Despite these benefits, interventionalists continue to face challenges in consistently acquiring and interpreting high-quality IVI data. The emergence of artificial intelligence (AI) offers a promising solution by enabling precise image recognition and automated quantitative analysis, thereby supporting efficient clinical decision-making and broadening the scope of IVI applications. This review aims to summarize recent research progress in AI-assisted IVI across the PCI process and discuss future directions to improve the intelligence and efficiency of intravascular imaging.