Advancing Vascular Surgery: The Role Of Artificial Intelligence And Machine Learning In Managing Carotid Stenosis

Authors

  • Ana Daniela Pias Faculdade de Medicina e Ciências Biomédicas da Universidade do Algarve, Faro, Portugal https://orcid.org/0009-0002-0989-6598
  • Juliana Pereira-Macedo Department of General Surgery – Unidade Local de Saúde do Médio Ave, Santo Tirso, Portugal; RISE@Health, Rua Dr. Plácido da Costa, Porto, Portugal
  • Ana Marreiros Faculdade de Medicina e Ciências Biomédicas da Universidade do Algarve, Faro, Portugal https://orcid.org/0000-0001-9410-4772
  • Nuno António NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Lisbon, Portugal https://orcid.org/0000-0002-4801-2487
  • João Rocha-Neves RISE@Health, Rua Dr. Plácido da Costa, Porto, Portugal; Department of Biomedicine – Unit of Anatomy, Faculdade de Medicina da Universidade do Porto, Portugal https://orcid.org/0000-0002-2656-8935

DOI:

https://doi.org/10.48729/pjctvs.411

Keywords:

Carotid stenosis, carotid endarterectomy, perioperative stroke

Abstract

Introduction: Cardiovascular diseases affect 17.7 million people annually, worldwide. Carotid degenerative disease, commonly described as atherosclerotic plaque accumulation, significantly contributes to this, posing a risk for cerebrovascular events and ischemic strokes. With carotid stenosis (CS) being a primary concern, accurate diagnosis, clinical staging, and timely surgical interventions, such as carotid endarterectomy (CEA), are crucial. This review explores the impact of Artificial Intelligence (AI) and Machine Learning (ML) in improving diagnosis, risk stratification, and management of CS.
Methods: A comprehensive literature review was conducted using PubMed and SCOPUS, focusing on AI and ML applications in diagnosing and managing extracranial CS. English language publications from the past two decades were reviewed, including cross-referenced scientific articles.
Results: Recent advancements in AI-enhanced imaging techniques, particularly in deep learning, have significantly improved diagnostic accuracy in identifying carotid plaque vulnerability and symptomatic plaques. Integration of clinical risk factors with AI systems has further enhanced precision. Additionally, ML models have shown promising results in identifying culprit arteries in patients with previous cerebrovascular events. These advancements hold immense potential for improving CS diagnosis and classification, leading to better patient management.
Conclusion: Integrating AI and ML into vascular surgery, particularly in managing CS, marks a transformative advancement. These technologies have significantly improved diagnostic accuracy and risk assessment, paving the way for more personalized and safer patient care. Despite clinical validation and data privacy challenges, AI and ML have immense potential for enhancing clinical decision-making in vascular surgery, marking a pivotal phase in the field's evolution.

Downloads

Download data is not yet available.

References

Jamthikar A, Gupta D, Khanna NN, Saba L, Araki T, Viskovic K, et al. A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes. Cardiovasc Diagn Ther. 2019;9(5):420-30.

Ambale-Venkatesh B, Yang X, Wu CO, Liu K, Hundley WG, Mc-Clelland R, et al. Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis. Circ Res. 2017;121(9):1092-101.

Naylor R, Rantner B, Ancetti S, de Borst GJ, De Carlo M, Halliday A, et al. Editor's Choice - European Society for Vascular Surgery (ESVS) 2023 Clinical Practice Guidelines on the Management of Atherosclerotic Carotid and Vertebral Artery Disease. Eur J Vasc Endovasc Surg. 2023;65(1):7-111.

Bennett KM, Kent KC, Schumacher J, Greenberg CC, Scarborough JE. Targeting the most important complications in vascular surgery. J Vasc Surg. 2017;65(3):793-803.

Pereira-Neves A, Rocha-Neves J, Fragao-Marques M, Duarte-Gamas L, Jacome F, Coelho A, et al. Red blood cell distribution width is associated with hypoperfusion in carotid endarterectomy under regional anesthesia. Surgery. 2021;169(6):1536-43.

Li B, Beaton D, Eisenberg N, Lee DS, Wijeysundera DN, Lindsay TF, et al. Using machine learning to predict outcomes following carotid endarterectomy. J Vasc Surg. 2023;78(4):973-87.e6.

Li B, Feridooni T, Cuen-Ojeda C, Kishibe T, de Mestral C, Mamdani M, et al. Machine learning in vascular surgery: a systematic review and critical appraisal. NPJ Digit Med. 2022;5(1):7.

Lareyre F, Chaudhuri A, Behrendt CA, Pouhin A, Teraa M, Boyle JR, et al. Artificial intelligence-based predictive models in vascular diseases. Semin Vasc Surg. 2023;36(3):440-7.

Stella A. The Way we were: Technology will Change the Profession of Vascular Surgery. Transl Med UniSa. 2020;21:52-8.

Pereira-Macedo J, Lopes-Fernandes B, Duarte-Gamas L, Pereira-Neves A, Mourao J, Khairy A, et al. The Gupta Perioperative Risk for Myocardial Infarct or Cardiac Arrest (MICA) Calculator as an Intraoperative Neurologic Deficit Predictor in Carotid Endarterectomy. J Clin Med. 2022;11(21).

Carreira M, Duarte-Gamas L, Rocha-Neves J, Andrade JP, Fernando-Teixeira J. Management of The Carotid Artery Stenosis in Asymptomatic Patients. Rev Port Cir Cardiotorac Vasc. 2020;27(3):159-66.

Latha S, Muthu P, Lai KW, Khalil A, Dhanalakshmi S. Performance Analysis of Machine Learning and Deep Learning Architectures on Early Stroke Detection Using Carotid Artery Ultrasound Images. Front Aging Neurosci. 2021;13:828214.

JM UK-I, Trivedi RA, Cross JJ, Higgins NJ, Hollingworth W, Graves M, et al. Measuring carotid stenosis on contrast-enhanced magnetic resonance angiography: diagnostic performance and reproducibility of 3 different methods. Stroke. 2004;35(9):2083-8.

Guang Y, He W, Ning B, Zhang H, Yin C, Zhao M, 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.

Saba L, Sanagala SS, Gupta SK, Koppula VK, Johri AM, Khanna NN, et al. Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application. Ann Transl Med. 2021;9(14):1206.

Lekadir K, Galimzianova A, Betriu A, Del Mar Vila M, Igual L, Rubin DL, et al. A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound. IEEE J Biomed Health Inform. 2017;21(1):48-55.

Huang Z, Cheng XQ, Liu HY, Bi XJ, Liu YN, Lv WZ, et al. Relation of Carotid Plaque Features Detected with Ultrasonography-Based Radiomics to Clinical Symptoms. Transl Stroke Res. 2022;13(6):970-82.

Yeh CY, Lee HH, Islam MM, Chien CH, Atique S, Chan L, et al. Development and Validation of Machine Learning Models to Classify Artery Stenosis for Automated Generating Ultrasound Report. Diagnostics (Basel). 2022;12(12).

Le EPV, Rundo L, Tarkin JM, Evans NR, Chowdhury MM, Coughlin PA, et al. Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events. Sci Rep. 2021;11(1):3499.

Xia H, Yuan L, Zhao W, Zhang C, Zhao L, Hou J, et al. Predicting transient ischemic attack risk in patients with mild carotid stenosis using machine learning and CT radiomics. Front Neurol. 2023;14:1105616.

Kigka VI, Sakellarios AI, Tsakanikas VD, Potsika VT, Koncar I, Fotiadis DI. Detection of Asymptomatic Carotid Artery Stenosis through Machine Learning. Annu Int Conf IEEE Eng Med Biol Soc. 2022;2022:1041-4.

Yu J, Zhou Y, Yang Q, Liu X, Huang L, Yu P, et al. Machine learning models for screening carotid atherosclerosis in asymptomatic adults. Sci Rep. 2021;11(1):22236.

Yin JX, Yu C, Wei LX, Yu CY, Liu HX, Du MY, et al. Detection of Asymptomatic Carotid Artery Stenosis in High-Risk Individuals of Stroke Using a Machine-Learning Algorithm. Chin Med Sci J. 2020;35(4):297-305.

Fan J, Chen M, Luo J, Yang S, Shi J, Yao Q, et al. The prediction of asymptomatic carotid atherosclerosis with electronic health records: a comparative study of six machine learning models. BMC Med Inform Decis Mak. 2021;21(1):115.

Poorthuis MHF, Halliday A, Massa MS, Sherliker P, Clack R, Morris DR, et al. Validation of Risk Prediction Models to Detect Asymptomatic Carotid Stenosis. J Am Heart Assoc. 2020;9(8):e014766.

Li B, Verma R, Beaton D, Tamim H, Hussain MA, Hoballah JJ, et al. Predicting Major Adverse Cardiovascular Events Following Carotid Endarterectomy Using Machine Learning. J Am Heart Assoc. 2023;12(20):e030508.

Bai P, Zhou Y, Liu Y, Li G, Li Z, Wang T, et al. Risk Factors of Cerebral Infarction and Myocardial Infarction after Carotid Endarterectomy Analyzed by Machine Learning. Comput Math Methods Med. 2020;2020:6217392.

Matsuo K, Fujita A, Hosoda K, Tanaka J, Imahori T, Ishii T, et al. Potential of machine learning to predict early ischemic events after carotid endarterectomy or stenting: a comparison with surgeon predictions. Neurosurg Rev. 2022;45(1):607-16.

Tan J, Wang Q, Shi W, Liang K, Yu B, Mao Q. A Machine Learning Approach for Predicting Early Phase Postoperative Hypertension in Patients Undergoing Carotid Endarterectomy. Ann Vasc Surg. 2021;71:121-31.

DeMartino RR, Brooke BS, Neal D, Beck AW, Conrad MF, Arya S, et al. Development of a validated model to predict 30-day stroke and 1-year survival after carotid endarterectomy for asymptomatic stenosis using the Vascular Quality Initiative. J Vasc Surg. 2017;66(2):433-44.e2.

Asaadi S, Martins KN, Lee MM, Pantoja JL. Artificial intelligence for the vascular surgeon. Semin Vasc Surg. 2023;36(3):394-400.

Dossabhoy SS, Ho VT, Ross EG, Rodriguez F, Arya S. Artificial intelligence in clinical workflow processes in vascular surgery and beyond. Semin Vasc Surg. 2023;36(3):401-12.

Lareyre F, Yeung KK, Guzzi L, Di Lorenzo G, Chaudhuri A, Behrendt CA, et al. Artificial intelligence in vascular surgical decision making. Semin Vasc Surg. 2023;36(3):448-53.

Amin A, Cardoso SA, Suyambu J, Abdus Saboor H, Cardoso RP, Husnain A, et al. Future of Artificial Intelligence in Surgery: A Narrative Review. Cureus. 2024;16(1):e51631.

Alonso A, Siracuse JJ. Protecting patient safety and privacy in the era of artificial intelligence. Semin Vasc Surg. 2023;36(3):426-9.

Collins GS, Dhiman P, Andaur Navarro CL, Ma J, Hooft L, Reitsma JB, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021;11(7):e048008.

Thaxton C, Dardik A. Computer Science meets Vascular Surgery: Keeping a pulse on artificial intelligence. Semin Vasc Surg. 2023;36(3):419-25.

Downloads

Published

12-10-2024

How to Cite

1.
Pias AD, Pereira-Macedo J, Marreiros A, António N, Rocha-Neves J. Advancing Vascular Surgery: The Role Of Artificial Intelligence And Machine Learning In Managing Carotid Stenosis. Rev Port Cir Cardiotorac Vasc [Internet]. 2024 Oct. 12 [cited 2024 Dec. 4];31(3):55-64. Available from: https://pjctvs.com/index.php/journal/article/view/411

Issue

Section

Review Article

Categories

Most read articles by the same author(s)

<< < 1 2