Applications of Artificial Intelligence in Vascular Diseases

Abstract

In their comprehensive article, Lareyre et al. evaluated scientific publications on artificial intelligence (AI) on noncardiac vascular diseases. 1 The authors provided a quantitative assessment of research output on publications related to AI in carotid artery stenosis, aortic and peripheral artery disease. 1 The bibliometric analysis of original articles published on AI in non-cardiac vascular diseases showed increased numbers over the past 5 years. 1 AI, with its numerous applications, has emerged as an indispensable part of healthcare. By means of its 2 subcategories, namely, machine learning (ML) and deep learning (DL), AI has expanded over the field of medical imaging and medical diagnosis to cover all aspects of vascular disease, from diagnosis to staging and treatment. 2,3 Atherosclerotic plaque formation in the carotid or peripheral arteries is a dynamic process. Advancements in ML and DL have made it possible to identify, classify and characterize the atherosclerotic plaque. 4 The nature of AI design has evolved over time, starting from manual, signal processing methods and eventually leading to AI learning models. 4 ML can be used for plaque characterization based on ultrasound, computed tomography (CT) or magnetic resonance angiography (MRI). While AI models may independently operate on each imaging technique, there are differences and similarities covering various aspects, such as multiresolution, extracted and texture/histogram features, performance metrics, segmentation and classification. 4 Diet and nutrition contribute to cardiovascular events in countries with high socio-demographic index. 5,6 Defining the role of nutrition in the formation of carotid atherosclerosis can determine cerebrovascular/cardiovascular events and all-cause mortality. 5,6 Another way to improve risk assessment is to use AI-based algorithms for cardiovascular risk stratification. 7 Such AI-based algorithms can handle a large number of risk predictors in a model. 7 Furthermore, since AI-based algorithms are data-specific algorithms, they capture and learn from the complex non-linear interrelationship among the risk predictors. 7 Conventional cardiovascular risk calculators (e.g. the Framingham risk score) have certain drawbacks when

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