The application of artificial intelligence(AI)in carotid atherosclerotic plaque detection via computed tomography angiography(CTA)has significantly ad-vanced over the past decade.This mini-review consolidates recent i...The application of artificial intelligence(AI)in carotid atherosclerotic plaque detection via computed tomography angiography(CTA)has significantly ad-vanced over the past decade.This mini-review consolidates recent innovations in deep learning architectures,domain adaptation techniques,and automated pl-aque characterization methodologies.Hybrid models,such as residual U-Net-Pyramid Scene Parsing Network,exhibit a remarkable precision of 80.49%in plaque segmentation,outperforming radiologists in diagnostic efficiency by reducing analysis time from minutes to mere seconds.Domain-adaptive fra-meworks,such as Lesion Assessment through Tracklet Evaluation,demonstrate robust performance across heterogeneous imaging datasets,achieving an area under the curve(AUC)greater than 0.88.Furthermore,novel approaches inte-grating U-Net and Efficient-Net architectures,enhanced by Bayesian optimi-zation,have achieved impressive correlation coefficients(0.89)for plaque quanti-fication.AI-powered CTA also enables high-precision three-dimensional vascular segmentation,with a Dice coefficient of 0.9119,and offers superior cardiovascular risk stratification compared to traditional Agatston scoring,yielding AUC values of 0.816 vs 0.729 at a 15-year follow-up.These breakthroughs address key challenges in plaque motion analysis,with systolic retractive motion biomarkers successfully identifying 80%of vulnerable plaques.Looking ahead,future directions focus on enhancing the interpretability of AI models through explainable AI and leveraging federated learning to mitigate data heterogeneity.This mini-review underscores the transformative potential of AI in carotid plaque assessment,offering substantial implic-ations for stroke prevention and personalized cerebrovascular management strategies.展开更多
基金Supported by Henan Province International Science and Technology Cooperation Project,2024,No.242102520054.
文摘The application of artificial intelligence(AI)in carotid atherosclerotic plaque detection via computed tomography angiography(CTA)has significantly ad-vanced over the past decade.This mini-review consolidates recent innovations in deep learning architectures,domain adaptation techniques,and automated pl-aque characterization methodologies.Hybrid models,such as residual U-Net-Pyramid Scene Parsing Network,exhibit a remarkable precision of 80.49%in plaque segmentation,outperforming radiologists in diagnostic efficiency by reducing analysis time from minutes to mere seconds.Domain-adaptive fra-meworks,such as Lesion Assessment through Tracklet Evaluation,demonstrate robust performance across heterogeneous imaging datasets,achieving an area under the curve(AUC)greater than 0.88.Furthermore,novel approaches inte-grating U-Net and Efficient-Net architectures,enhanced by Bayesian optimi-zation,have achieved impressive correlation coefficients(0.89)for plaque quanti-fication.AI-powered CTA also enables high-precision three-dimensional vascular segmentation,with a Dice coefficient of 0.9119,and offers superior cardiovascular risk stratification compared to traditional Agatston scoring,yielding AUC values of 0.816 vs 0.729 at a 15-year follow-up.These breakthroughs address key challenges in plaque motion analysis,with systolic retractive motion biomarkers successfully identifying 80%of vulnerable plaques.Looking ahead,future directions focus on enhancing the interpretability of AI models through explainable AI and leveraging federated learning to mitigate data heterogeneity.This mini-review underscores the transformative potential of AI in carotid plaque assessment,offering substantial implic-ations for stroke prevention and personalized cerebrovascular management strategies.
