Wang DY, Yang T, Zhang CT, Zhan PC, Miao ZX, Li BL, Yang H. Artificial intelligence in carotid computed tomography angiography plaque detection: Decade of progress and future perspectives. World J Radiol 2025; 17(9): 110447 [PMID: 41025057 DOI: 10.4329/wjr.v17.i9.110447]
Corresponding Author of This Article
Hang Yang, Department of Nursing, The Third People’s Hospital of Henan Province, No. 346 Funiu Road, Zhengzhou 450000, Henan Province, China. p2413600@mpu.edu.mo
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Radiol. Sep 28, 2025; 17(9): 110447 Published online Sep 28, 2025. doi: 10.4329/wjr.v17.i9.110447
Artificial intelligence in carotid computed tomography angiography plaque detection: Decade of progress and future perspectives
Dong-Yang Wang, Tie Yang, Chong-Tao Zhang, Peng-Chao Zhan, Zhen-Xing Miao, Bing-Lin Li, Hang Yang
Dong-Yang Wang, Bing-Lin Li, Hang Yang, Department of Nursing, The Third People’s Hospital of Henan Province, Zhengzhou 450000, Henan Province, China
Tie Yang, Department of Publicity, The Third People’s Hospital of Henan Province, Zhengzhou 450000, Henan Province, China
Chong-Tao Zhang, Department of Vice President, The Third People’s Hospital of Henan Province, Zhengzhou 450000, Henan Province, China
Peng-Chao Zhan, Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
Zhen-Xing Miao, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 73170, Krung Thep Maha Nakhon, Thailand
Hang Yang, Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macau 999078, China
Co-first authors: Dong-Yang Wang and Tie Yang.
Author contributions: Wang D designed the framework and drafted the manuscript; Yang T performed literature analysis; Wang DY and Yang T contributed equally to this work as the co-first authors of the paper; Zhang CT supervised technical content; Zhan PC and Miao ZX collected clinical data; Li BL critically revised the manuscript; all of the authors read and approved the final version of the manuscript to be published.
Supported by Henan Province International Science and Technology Cooperation Project, 2024, No. 242102520054.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Hang Yang, Department of Nursing, The Third People’s Hospital of Henan Province, No. 346 Funiu Road, Zhengzhou 450000, Henan Province, China. p2413600@mpu.edu.mo
Received: June 9, 2025 Revised: August 2, 2025 Accepted: August 27, 2025 Published online: September 28, 2025 Processing time: 112 Days and 6.7 Hours
Abstract
The application of artificial intelligence (AI) in carotid atherosclerotic plaque detection via 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 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 implications for stroke prevention and personalized cerebrovascular management strategies.
Core Tip: This is the first mini-review to comprehensively analyze artificial intelligence (AI)-driven advancements in carotid computed tomography angiography plaque detection over ten years. We provide novel insights into hybrid deep learning architectures, domain adaptation techniques, and their clinical translation. The work establishes quantitative benchmarks for diagnostic performance and highlights future directions for explainable AI systems in vascular imaging.
