High-Risk Carotid Lesion Segmentation: Advancing Stroke Risk Detection With Deep Learning

This study demonstrates the potential of PRAEVAorta2 to automate carotid lesion analysis, offering promise for identifying high-risk asymptomatic plaques and therefore aid surgical decision-making. Symptomatic carotid lesions displayed higher thrombus volume, lower calcium content, and larger plaque volumes than asymptomatic lesions.Clinical ImpactThis study introduces an AI-based tool, PRAEVAorta2, capable of automatically segmenting and quantifying carotid plaque components on CT angiography. By distinguishing between thrombus and calcification, the tool provides a more nuanced assessment of plaque vulnerability beyond stenosis grading. Clinically, this innovation could enhance risk stratification in asymptomatic carotid stenosis, supporting more individualized decisions for surgery. The demonstrated correlation between thrombus burden and symptoms highlights the potential to identify high-risk plaques before neurological events occur. This advancement may bridge the gap between imaging and clinical decision-making, promoting proactive and targeted management of carotid artery disease.