Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research.


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Article
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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.

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Keywords
Arterial stiffness, Blood pressure, Cardiovascular, Central blood pressure, Machine learning, Pulse wave velocity
Journal Title
Eur Heart J Digit Health
Conference Name
Journal ISSN
2634-3916
2634-3916
Volume Title
Publisher
Oxford University Press (OUP)
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All rights reserved
Sponsorship
British Heart Foundation (FS/20/20/34626)