Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research
Vasiliki Bikia, Terence S. Fong, Rachel E. Climie, Rosa María Bruno, Bernhard Hametner, Christopher Mayer, Dimitrios Terentes‐Printzios, Peter Charlton
Abstract
Vascular ageing biomarkers have been found to be predictive of cardiovascular risk independently of classical risk factors, yet are not widely used in clinical practice. In this review, we present two basic approaches for using machine learning (ML) to assess vascular age: parameter estimation and risk classification. We then summarize their role in developing new techniques to assess vascular ageing quickly and accurately. We discuss the methods used to validate ML-based markers, the evidence for their clinical utility, and key directions for future research. The review is complemented by case studies of the use of ML in vascular age assessment which can be replicated using freely available data and code.