How AI Is Automating Coronary Artery Analysis in IVUS Imaging

Artificial intelligence is automating the analysis of intravascular ultrasound (IVUS) imaging for coronary arteries, aiming to make assessments faster and more consistent than traditional manual interpretation. This technology focuses on key tasks like segmentation and plaque characterization to improve diagnostic accuracy and patient outcomes.

Diego Navarro

October 3, 2026 · 4 min read

Artificial intelligence is being applied to intravascular ultrasound (IVUS) imaging to automate the analysis of coronary arteries, a process traditionally reliant on time-consuming manual interpretation by clinical experts. By training AI models to perform key analytical tasks, this technology aims to make the assessment of coronary artery disease faster and more consistent, potentially improving diagnostic accuracy and patient outcomes.

The Challenge of Manual IVUS Analysis

Intravascular ultrasound is a critical imaging tool used to guide percutaneous coronary interventions, the procedures used to open clogged heart arteries. It provides detailed, cross-sectional views from inside the blood vessel, allowing clinicians to assess the extent of disease. However, a systematic review highlights that the standard method for analyzing these images involves a "time-consuming, expert-dependent manual analysis."

This reliance on manual interpretation presents significant challenges. The process requires specialized expertise to identify vessel boundaries, measure plaque, and characterize its composition. According to the same review, the time-intensive nature of this analysis may be a factor in why IVUS, despite its proven benefits for improving patient outcomes, remains underused in everyday clinical practice.

Key AI Tasks: Segmentation and Plaque Characterization

AI applications in IVUS imaging focus on automating two primary analytical tasks: segmentation and plaque characterization. These functions form the foundation for quantitative analysis of coronary artery disease.

Automated Segmentation involves the AI model identifying and outlining the key structures within the IVUS image. According to guidance from the European Society of Cardiology, this includes delineating the lumen, which is the open channel through which blood flows, and the outer vessel wall. By precisely tracing these borders in each image frame, the AI can automatically calculate critical measurements such as the lumen area and the overall plaque burden—the percentage of the vessel wall thickened by plaque.

Plaque Characterization and Analysis goes a step further by examining the composition of the atherosclerotic plaque itself. AI models can be trained to identify different tissue types within the plaque buildup. This includes tasks like identifying calcium deposits, a key marker of advanced disease, and assessing the overall structure of the plaque. This automated analysis also extends to evaluating the placement and performance of stents, ensuring they are properly expanded and positioned against the vessel wall.

The AI-Powered IVUS Analysis Workflow

Artificial intelligence transforms the raw data from an IVUS catheter into clinically actionable measurements through a streamlined, automated workflow. This process replaces the meticulous, frame-by-frame manual tracing and calculation with a rapid, algorithm-driven analysis.

Image Acquisition: A standard IVUS procedure is performed, where a catheter with a tiny ultrasound probe is guided through the coronary artery, capturing a series of cross-sectional images.
Automated Segmentation: The AI algorithm processes the IVUS image series. Its first task is segmentation, where it automatically traces the vascular boundaries—the lumen and the external vessel wall—in each frame.
Plaque Analysis: With the vessel structures defined, the AI then characterizes the tissue composition of the plaque located between the lumen and the vessel wall. This can include identifying specific components like calcium.
Quantitative Measurement: Finally, the system uses the segmented and characterized data to perform a quantitative analysis. It automatically calculates key metrics such as lumen area, plaque burden, and assesses stent expansion, providing the clinician with a comprehensive, data-driven report.