Artificial intelligence in carotid computed tomography angiography plaque detection: Decade of progress and future perspectives.
Authors
Affiliations (6)
Affiliations (6)
- Department of Nursing, The Third People's Hospital of Henan Province, Zhengzhou 450000, Henan Province, China.
- Department of Publicity, The Third People's Hospital of Henan Province, Zhengzhou 450000, Henan Province, China.
- Department of Vice President, The Third People's Hospital of Henan Province, Zhengzhou 450000, Henan Province, China.
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China.
- Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 73170, Krung Thep Maha Nakhon, Thailand.
- Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macau 999078, China. [email protected].
Abstract
The application of artificial intelligence (AI) in carotid atherosclerotic plaque detection <i>via</i> computed tomography angiography (CTA) has significantly advanced over the past decade. This mini-review consolidates recent innovations in deep learning architectures, domain adaptation techniques, and automated plaque 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 frameworks, 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 integrating U-Net and Efficient-Net architectures, enhanced by Bayesian optimization, have achieved impressive correlation coefficients (0.89) for plaque quantification. 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 <i>vs</i> 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 implications for stroke prevention and personalized cerebrovascular management strategies.