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

Citation

Bikia, V and Fong, T and Climie, RE and Bruno, RM and Hametner, B and Mayer, C and Terentes-Printzios, D and Charlton, PH, Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research, European Heart Journal - Digital Health, 2, (4) pp. 676-690. ISSN 2634-3916 (2021) [Refereed Article]


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Copyright 2021 The Authors

DOI: doi:10.1093/ehjdh/ztab089

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.

Item Details

Item Type:Refereed Article
Keywords:Arterial stiffness, blood pressure, cardiovascular, central blood pressure, pulse wave velocity, machine learning, vascular ageing
Research Division:Biomedical and Clinical Sciences
Research Group:Cardiovascular medicine and haematology
Research Field:Cardiology (incl. cardiovascular diseases)
Objective Division:Health
Objective Group:Clinical health
Objective Field:Prevention of human diseases and conditions
UTAS Author:Climie, RE (Dr Rachel Climie)
ID Code:149543
Year Published:2021
Deposited By:Menzies Institute for Medical Research
Deposited On:2022-04-04
Last Modified:2022-05-18
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