Machine learning-based classification of carotid plaques via ultrasound: a systematic review and meta-analysis of diagnostic performance | International Journal of Emergency Medicine

Strilciuc S, Grad DA, Radu C, et al. The economic burden of stroke: a systematic review of cost of illness studies. J Med Life. 2021;14(5):606–19. https://doi.org/10.25122/jml-2021-0361.

Google Scholar 

Global, regional, and national burden of stroke and its risk factors, 1990-2021: a systematic analysis for the global burden of disease study 2021. Lancet Neurol. 2024;23(10):973–1003. https://doi.org/10.1016/s1474-4422(24)00369-7.

Google Scholar 

Martin SS, Aday AW, Allen NB, et al. Heart disease and stroke statistics: a report of US and global data from the American Heart Association. Circulation. 2025 2025;151(8):e41–660. https://doi.org/10.1161/cir.0000000000001303.

Cheng Y, Lin Y, Shi H, et al. Projections of the stroke burden at the global, regional, and national levels up to 2050 based on the global burden of disease study 2021. J Am Heart Assoc. 2024;13(23):e036142. https://doi.org/10.1161/jaha.124.036142.

Google Scholar 

Diener HC, Easton JD, Hart RG, Kasner S, Kamel H, Ntaios G. Review and update of the concept of embolic stroke of undetermined source. Nat Rev Neurol. 2022;18(8):455–65. https://doi.org/10.1038/s41582-022-00663-4.

Google Scholar 

Kamtchum-Tatuene J, Wilman A, Saqqur M, Shuaib A, Jickling GC. Carotid plaque with high-risk features in embolic stroke of undetermined source: systematic review and meta-analysis. Stroke. 2020;51(1):311–14. https://doi.org/10.1161/strokeaha.119.027272.

Google Scholar 

Ospel JM, Marko M, Singh N, Goyal M, Almekhlafi MA. Prevalence of non-stenotic (<50%) carotid plaques in acute ischemic stroke and transient ischemic attack: a systematic review and meta-analysis. J Stroke Cerebrovasc Dis. 2020;29(10):105117. https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105117.

Google Scholar 

Sajjadi SM, Mohebbi A, Ehsani A, et al. Identifying abdominal aortic aneurysm size and presence using natural language processing of radiology reports: a systematic review and meta-analysis. Abdominal Radiol. 2025. https://doi.org/10.1007/s00261-025-04810-5.

Google Scholar 

Eini P, Eini P, Serpoush H, Rezayee M, Tremblay J. Machine learning models for carotid artery plaque detection: a systematic review of ultrasound-based diagnostic performance. J Stroke Cerebrovascular Dis. 2025;34(11):108446. https://doi.org/10.1016/j.jstrokecerebrovasdis.2025.108446.

Google Scholar 

Biswas M, Saba L, Kalra M, et al. MultiNet 2.0: a lightweight attention-based deep learning network for stenosis measurement in carotid ultrasound scans and cardiovascular risk assessment. Computerized Med Imag Graphics. 2024;117:102437. https://doi.org/10.1016/j.compmedimag.2024.102437.

Google Scholar 

Chen X-X, Kong Z-X, Wei S-F, et al. Ultrasound lmaging-vulnerable plaque diagnostics: automatic carotid plaque segmentation based on deep learning. J Radiat Res Appl Sci. 2023;16(3):100598. https://doi.org/10.1016/j.jrras.2023.100598.

Google Scholar 

Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. https://doi.org/10.1136/bmj.n71.

Google Scholar 

Moons KGM, Damen JAA, Kaul T, et al. PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ. 2025;388:e082505. https://doi.org/10.1136/bmj-2024-082505.

Google Scholar 

Singh S, Jain PK, Sharma N, Pohit M, Roy S. Atherosclerotic plaque classification in carotid ultrasound images using machine learning and explainable deep learning. Intell Med. 2024;4(2):83–95. https://doi.org/10.1016/j.imed.2023.05.003.

Google Scholar 

Zhou X, Huang Y, Xue W, et al. Inflated 3D convolution-transformer for weakly-supervised carotid stenosis grading with ultrasound videos. In: Greenspan H, Madabhushi A, Mousavi P, et al., editors. Medical image computing and computer assisted intervention – MICCAI 2023. Cham: Springer Nature Switzerland; 2023. p. 511–20.

Kybic J, Pakizer D, Kozel J, Michalčová P, Charvát F, Školoudík D. Atherosclerotic plaque stability prediction from longitudinal ultrasound images. In: Xu X, Cui Z, Rekik I, Ouyang X, Sun K, editors. Machine learning in medical imaging. Cham: Springer Nature Switzerland; 2025. p. 124–32.

Google Scholar 

Jain PK, Sharma N, Roy S. Hybrid deep learning models for segmentation of atherosclerotic plaque in B-mode carotid ultrasound image. In: Sharma S, Subudhi B, Sahu U, editors. Intelligent control, robotics, and industrial automation. Singapore: Springer Nature Singapore; 2023. p. 807–19.

Google Scholar 

Kigka VI, Sakellarios AI, Mantzaris MD, et al. A machine learning model for the identification of high-risk carotid atherosclerotic plaques. Annu Int Conf IEEE Eng Med Biol Soc. 2021;2021:2266–69. https://doi.org/10.1109/embc46164.2021.9630654.

Google Scholar 

Lindsey T, Garami Z. Automated stenosis classification of carotid artery sonography using deep neural networks. 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA). 2019:1880–84.

Azzopardi C, Camilleri KP, Hicks YA. Bimodal automated carotid ultrasound segmentation using geometrically constrained deep neural networks. IEEE J Biomed Health Inf. 2020;24(4):1004–15. https://doi.org/10.1109/jbhi.2020.2965088.

Google Scholar 

Guang Y, He W, Ning B, et al. Deep learning-based carotid plaque vulnerability classification with multicentre contrast-enhanced ultrasound video: a comparative diagnostic study. BMJ Open. 2021;11(8):e047528. https://doi.org/10.1136/bmjopen-2020-047528.

Google Scholar 

Yoshidomi T, Kume S, Aizawa H, Furui A. Classification of carotid plaque with jellyfish sign through convolutional and recurrent neural networks utilizing plaque surface edges. 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC):1–42024.

Kybic J, Pakizer D, Kozel J, Michalčová P, Charvát F, Školoudík D. Predikce stability aterosklerotického plátu z transverzálních ultrazvukových obrazů pomocí hlubokého učení. Čes Slov Neurol Neurochir. 2024;87(4).

Jain PK, Sharma N, Saba L, et al. Unseen artificial intelligence—deep learning paradigm for segmentation of low atherosclerotic plaque in carotid ultrasound: a multicenter cardiovascular study. Diagnostics.

Yuan Y, Li C, Zhang K, Hua Y, Zhang J. HRU-Net: a transfer learning method for carotid artery plaque segmentation in ultrasound images. Diagnostics.

Jain PK, Dubey A, Saba L, et al. Attention-based UNet deep learning model for plaque segmentation in carotid ultrasound for stroke risk stratification: an artificial intelligence paradigm. J Cardiovasc Dev Dis.

Liu M, Gao W, Song D, et al. A deep learning-based calculation system for plaque stenosis severity on common carotid artery of ultrasound images. Vascular. 2024;17085381241246312. https://doi.org/10.1177/17085381241246312.

Jain PK, Sharma N, Saba L, et al. Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: an asymptomatic Japanese cohort study. Int Angiol. 2022;41(1):9–23. https://doi.org/10.23736/s0392-9590.21.04771-4.

Saba L, Sanagala SS, Gupta SK, 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 Imag. 2021;37(5):1511–28. https://doi.org/10.1007/s10554-020-02124-9.

Google Scholar 

Saba L, Sanagala SS, Gupta SK, et al. A multicenter study on carotid ultrasound plaque tissue characterization and classification using six deep artificial intelligence models: a stroke application. IEEE Trans Instrum Meas. 2021;70:1–12. https://doi.org/10.1109/TIM.2021.3052577.

Google Scholar 

Ganitidis T, Athanasiou M, Dalakleidi K, Melanitis N, Golemati S, Nikita KS. Stratification of carotid atheromatous plaque using interpretable deep learning methods on B-mode ultrasound images. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC):3902–052021.

Kassem M, Florea A, Mottaghy FM, van Oostenbrugge R, Kooi ME. Magnetic resonance imaging of carotid plaques: current status and clinical perspectives. Ann Transl Med. 2020;8(19):1266. https://doi.org/10.21037/atm-2020-cass-16.

Google Scholar 

Porambo ME, DeMarco JK. Mr imaging of vulnerable carotid plaque. Cardiovasc Diagn Ther. 2020;10(4):1019–31. https://doi.org/10.21037/cdt.2020.03.12.

Google Scholar 

Kassem M, Nies KPH, Boswijk E, et al. Quantification of carotid plaque composition with a multi-contrast atherosclerosis characterization (match) MRI sequence. Front Cardiovasc Med. 2023;10:1227495. https://doi.org/10.3389/fcvm.2023.1227495.

Google Scholar 

Hou C, Li S, Zheng S, et al. Quality assessment of radiomics models in carotid plaque: a systematic review. Quant Imag Med Surg. 2023;14(1):1141–54. https://qims.amegroups.org/article/view/119159.

Google Scholar 

Hou C, Liu X-Y, Du Y, et al. Radiomics in carotid plaque: a systematic review and radiomics quality score assessment. Ultrasound Med Biol. 2023;49(12):2437–45. https://doi.org/10.1016/j.ultrasmedbio.2023.06.008.

Google Scholar 

Rafailidis V, Chryssogonidis I, Grisan E, et al. Does quantification of carotid plaque surface irregularities better detect symptomatic plaques compared to the subjective classification? J Ultrasound Med. 2019;38(12):3163–71. https://doi.org/10.1002/jum.15017.

Google Scholar 

Baldassarre D, Hamsten A, Veglia F, et al. Measurements of carotid intima-media thickness and of interadventitia common carotid diameter improve prediction of cardiovascular events: results of the improve (carotid intima media thickness [IMT] and IMT-progression as predictors of vascular events in a high risk European population) study. J Am Coll Cardiol. 2012;60(16):1489–99. https://doi.org/10.1016/j.jacc.2012.06.034.

Google Scholar 

Sonaglioni A, Caminati A, Lipsi R, Lombardo M, Harari S. Association between C-reactive protein and carotid plaque in mild-to-moderate idiopathic pulmonary fibrosis. Intern Emerg Med. 2021;16(6):1529–39. https://doi.org/10.1007/s11739-020-02607-6.

Google Scholar 

Luo X, Li W, Bai Y, Du L, Wu R, Li Z. Relation between carotid vulnerable plaques and peripheral leukocyte: a case-control study of comparison utilizing multi-parametric contrast-enhanced ultrasound. BMC Med Imag. 2019;19(1):74. https://doi.org/10.1186/s12880-019-0374-9.

Google Scholar 

Dong H, Du T, Premaratne S, et al. Relationship between ADAMTS4 and carotid atherosclerotic plaque vulnerability in humans. J Vasc Surg. 2018;67(4):1120–26. https://doi.org/10.1016/j.jvs.2017.08.075.

Google Scholar 

Costanzo P, Perrone-Filardi P, Vassallo E, et al. Does carotid intima-media thickness regression predict reduction of cardiovascular events? A meta-analysis of 41 randomized trials. J Am Coll Cardiol. 2010;56(24):2006–20. https://doi.org/10.1016/j.jacc.2010.05.059.

Google Scholar 

Willeit P, Tschiderer L, Allara E, et al. Carotid intima-media thickness progression as surrogate marker for cardiovascular risk: meta-analysis of 119 clinical trials involving 100 667 patients. Circulation. 2020;142(7):621–42. https://doi.org/10.1161/circulationaha.120.046361.

Google Scholar 

Zhang Y, Guallar E, Malhotra S, et al. Carotid artery wall thickness and incident cardiovascular events: a comparison between US and MRI in the multi-ethnic study of atherosclerosis (MESA). Radiology. 2018;289(3):649–57. https://doi.org/10.1148/radiol.2018173069.

Google Scholar 

Zhou S, Hui P. Predictive value of contrast-enhanced carotid ultrasound features for stroke risk: a systematic review and meta-analysis. Front Neurol. 2025;16:1487850. https://doi.org/10.3389/fneur.2025.1487850.

Google Scholar 

Jashari F, Ibrahimi P, Bajraktari G, Grönlund C, Wester P, Henein MY. Carotid plaque echogenicity predicts cerebrovascular symptoms: a systematic review and meta-analysis. Eur J Neurol. 2016;23(7):1241–47. https://doi.org/10.1111/ene.13017.

Google Scholar