Key result
AI using ultrasound, CT, and MRI shows promise for automated detection of vulnerable carotid plaques.
Why the study?
Plaque structure and composition determine vulnerability beyond luminal stenosis alone, and AI-based carotid imaging techniques can offer solutions for tissue characterization and classification.
Does the application of artificial intelligence-based models improve the detection and characterization of vulnerable carotid plaque in imaging?
Does the application of artificial intelligence-based models improve the detection and characterization of vulnerable carotid plaque in imaging?
AI-based models offer promising, highly accurate automated solutions for segmenting and characterizing vulnerable carotid plaques across multiple imaging modalities, potentially improving stroke risk stratification.
Answers are grounded in this paper and cite their sources.
May support AI-assisted carotid imaging analysis but should not yet change practice; leaves open need for prospective outcome validation.
Carotid atherosclerotic disease is a widely acknowledged risk factor for ischemic stroke, making it a major concern on a global scale. To alleviate the socio-economic impact of carotid atherosclerotic disease, crucial objectives include prioritizing prevention efforts and early detection. So far, the degree of carotid stenosis has been regarded as the primary parameter for risk assessment and determining appropriate therapeutic interventions. Histopathological and imaging-based studies demonstrated important differences in the risk of cardiovascular events given a similar degree of luminal stenosis, identifying plaque structure and composition as key determinants of either plaque vulnerability or stability. The application of Artificial Intelligence (AI)-based techniques to carotid imaging can offer several solutions for tissue characterization and classification. This review aims to present a comprehensive overview of the main concepts related to AI. Additionally, we review the existing literature on AI-based models in ultrasound (US), computed tomography (CT), and Magnetic Resonance Imaging (MRI) for vulnerable plaque detection, and we finally examine the advantages and limitations of these AI approaches.
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Cau et al. (2023) conducted a review in Vulnerable carotid plaque. Artificial intelligence-based models was evaluated. Artificial intelligence-based models using ultrasound, computed tomography, and magnetic resonance imaging demonstrate promising capabilities for the automated detection of vulnerable carotid plaques.
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