Artificial intelligence (AI) has emerged as a transformative tool in ultrasound imaging enhancing the accuracy and efficiency of carotid plaque characterization. In addition, AI-driven models facilitate automated analysis reducing interobserver variability and enhancing reproducibility in plaque assessment.
Materials and Methods:
A systematic review process is outlined that provides an observational study curated from the available results from 1165 unique studies and were screened based on their titles and abstracts.
Results:
This review is based on 11 studies that satisfied all eligibility requirements and were included in the final review synthesis. The summative results highlighted the current state-of-the-art applications of AI in sonography, as well as providing early detection and risk stratification of carotid atherosclerotic plaques. The review also addressed emerging technologies, limitations, and future directions in AI-driven vascular imaging.
Conclusion:
With continued advancements, AI has the potential to revolutionize carotid sonography, enabling early intervention and reducing the global burden of stroke and cardiovascular diseases.
MiceliGBassoMGPintusC, et al: Molecular pathways of vulnerable carotid plaques at risk of ischemic stroke: a narrative review. Int J Mol Sci2024;25(8):4351. doi:10.3390/ijms25084351.
2.
BadimonLVilahurG: Thrombosis formation on atherosclerotic lesions and plaque rupture. J Intern Med2014;276(6):618–632. doi:10.1111/joim.12296.
3.
SaxenaANgEYKLimST: Imaging modalities to diagnose carotid artery stenosis: progress and prospect. Biomed Eng Online2019;18:1–23. doi:10.1186/s12938-019-0685-7.
4.
WangDDLinSLyuGR: Advances in the application of artificial intelligence in the ultrasound diagnosis of vulnerable carotid atherosclerotic plaque. Ultrasound Med Biol2025;51(4):607–614. doi:10.1016/j.ultrasmedbio.2024.12.010.
5.
CostanzoLFaillaGAluigiL, et al: Operative procedures for ultrasound assessment of extracranial artery disease: a narrative review by the Italian society for vascular investigation (SIDV). J Clin Med2025;14(19):7050. doi:10.3390/jcm14197050.
6.
YanLLiQFuKZhouXZhangK: Progress in the application of artificial intelligence in ultrasound-assisted medical diagnosis. Bioengineering2025;12(3):288. doi:10.3390/bioengineering12030288.
7.
MiceliGRizzoGBassoMG, et al: Artificial intelligence in symptomatic carotid plaque detection: a narrative review. Appl Sci2023;13(7):4321. doi:10.3390/app13074321.
8.
WangD-YYangTZhangC-T, et al: Artificial intelligence in carotid computed tomography angiography plaque detection: decade of progress and future perspectives. World J Radiol2025;17(9):110447. doi:10.4329/wjr.v17.i9.110447.
9.
GuptaAGuptaVOjhaA: Transforming cardiovascular health: an emerging role of biomarkers, artificial intelligence-driven imaging, and digital-based technologies. Digit Technol Med2025;1(1):316–328. doi:10.1089/dtom.2025.0034.
10.
AlexandratouMPapachristodoulouALiX, et al: Advances in noninvasive carotid wall imaging with ultrasound: a narrative review. J Clin Med2022;11(20):6196. doi:10.3390/jcm11206196.
11.
SavaşSTopaloğluNKazcı KoşarPN: Classification of carotid artery intima media thickness ultrasound images with deep learning. J Med Syst2019;43(8):273. doi:10.1007/s10916-019-1406-2.
12.
SudhaSKBJMadianN: Convolutional neural network for segmentation and measurement of intima media thickness. J Med Syst2018;42(8):1–8. doi:10.1007/s10916-018-1001-y.
13.
BiswasMKuppiliVSabaL, et al: Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: a tool for stroke risk. Med Biol Eng Comput2019;57(2):543–564. doi:10.1007/s11517-018-1897-x.
14.
BiswasMSabaLChakrabarttyS, et al: Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: a screening tool for cardiovascular/stroke risk assessment. Comput Biol Med2020;123:103847. doi:10.1016/j.compbiomed.2020.103847.
15.
WeiYYangBWeiL, et al: Real-time carotid plaque recognition from dynamic ultrasound videos based on artificial neural network. Ultraschall Med2024;45(5):493–500. doi:10.1055/a-2180-8405.
16.
LekadirKGalimzianovaABetriuA, et al: A convolutional neural network for automatic characterization of plaque composition in carotid ultrasound. IEEE J Biomed Health Inform2016;21(1):48–55. doi:10.1109/JBHI.2016.2631401.
17.
SuSSLiLYWangYLiYZ: Stroke risk prediction by color Doppler ultrasound of carotid artery-based deep learning using inception V3 and VGG-16. Front Neurol2023;14:1111906. doi:10.3389/fneur.2023.1111906.
18.
SabaLSanagalaSSGuptaSK, et al: Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: a cardiovascular disease/stroke risk assessment system. Int J Cardiovasc Imaging2021;37(5):1511–1528. doi:10.1007/s10554-020-02124-9.
19.
SkandhaSSGuptaSKSabaL, et al: 3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular/stroke risk stratification using carotid ultrasound-based delineated plaque: atheromatic™ 2.0. Comput Biol Med2020;125:103958. doi:10.1016/j.compbiomed.2020.103958.
20.
JainPKSharmaNSabaL, et al: Unseen artificial intelligence—deep learning paradigm for segmentation of low atherosclerotic plaque in carotid ultrasound: a multicenter cardiovascular study. Diagnostics2021;11(12):2257. doi:10.3390/diagnostics11122257.
21.
JainPKSharmaNGiannopoulosAASabaLNicolaidesASuriJS: Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound. Comput Biol Med2021;136:104721. doi:10.1016/j.compbiomed.2021.104721.
22.
AkkusZCaiJBoonrodA, et al: A survey of deep-learning applications in ultrasound: artificial intelligence–powered ultrasound for improving clinical workflow. J Am Coll Radiol2019;16(9, pt B):1318–1328. doi:10.1016/j.jacr.2019.06.004.
23.
FöllmerBWilliamsMCDeyD, et al: Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries. Nat Rev Cardiol2024;21(1):51–64. doi:10.1038/s41569-023-00900-3.
24.
DangiRRSharmaAVageriyaV: Transforming healthcare in low-resource settings with artificial intelligence: recent developments and outcomes. Public Health Nurs2025;42(2):1017–1030. doi:10.1111/phn.13500.
25.
El ArabRAAbu-MahfouzMSAbuadasFH, et al: Bridging the gap: from AI success in clinical trials to real-world healthcare implementation—a narrative review. Healthcare2025;13(7):701. doi:10.3390/healthcare13070701.
26.
SelvapriyaBDivyaS: Ethical and regulatory dimensions of integrating AI insights on nuclear medicine. Adv Nucl Med2025;24:1. doi:10.4018/979-8-3373-1275-0.ch001.
27.
Pérez-SanpabloAIQuinzaños-FresnedoJGutiérrez-MartínezJLozano- RodríguezIGRoldan-ValadezE: Transforming medical imaging: the role of artificial intelligence integration in PACS for enhanced diagnostic accuracy and workflow efficiency. Curr Med Imaging2025;21(1):e15734056370620. doi:10.2174/0115734056370620250403030638.
28.
Al- ZoghbyAMIsmail EbadaASalehASAbdelhayMAwadWA: A comprehensive review of multimodal deep learning for enhanced medical diagnostics. Comput Mater Contin2025;84(3):4155–4193. doi:10.32604/cmc.2025.065571.
29.
LiuZYKuoCF: Artificial intelligence-driven personalized medicine. Hand Clin2025;42:65–74. doi:10.1016/j.hcl.2025.08.008.
30.
GalaDBehlHShahMMakaryusAN: The role of artificial intelligence in improving patient outcomes and future of healthcare delivery in cardiology: a narrative review of the literature. Healthcare2024;12(4):481. doi:10.3390/healthcare12040481.
