Key result
AI radiomics and machine learning outperform traditional imaging in detecting high-risk vulnerable carotid plaques.
Why the study?
Traditional carotid imaging methods rely on luminal stenosis and qualitative plaque features but lack sufficient sensitivity for predicting stroke risk.
Systematic Review
AI and radiomics facilitate a paradigm shift in carotid risk assessment from evaluating lumen stenosis to identifying plaque vulnerability, offering new avenues for personalized stroke prevention.
AI radiomics improves high-risk carotid plaque detection beyond stenosis; supports integration into stroke risk stratification and prospective outcome trials.
Vulnerable carotid plaques are a major cause of ischemic stroke.Traditional imaging assessment methods primarily rely on the degree of luminal stenosis and qualitative plaque characteristics, but they lack sufficient sensitivity in predicting stroke risk.In recent years, artificial intelligence (AI)-based radiomics and machine learning (ML) technologies have enabled high-throughput quantitative analysis of medical images, significantly improving the ability to identify high-risk plaques.This study provides a systematic review of the latest advances in the application of radiomics and machine learning to the assessment of carotid artery plaques, with a focus on imaging modalities such as ultrasound (US), computed tomography angiography (CTA), and magnetic resonance imaging (MRI).
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Songrui Zhu (2026) conducted a systematic review in Carotid Plaques. Artificial Intelligence and Radiomics vs. Traditional imaging assessment was evaluated. Artificial intelligence-based radiomics and machine learning technologies significantly improve the ability to identify high-risk vulnerable carotid plaques compared to traditional imaging methods.
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