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

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.

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.

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.

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.