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Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research
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Bikia, V, Fong, T, Climie, RE, Bruno, RM, Hametner, B, Mayer, C, Terentes-Printzios, D and Charlton, PH 2021
, 'Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research'
, European Heart Journal - Digital Health, vol. 2, no. 4
, 676–690
, doi: https://doi.org/10.1093/ehjdh/ztab089.
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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.
| Item Type: | Article |
|---|---|
| Authors/Creators: | Bikia, V and Fong, T and Climie, RE and Bruno, RM and Hametner, B and Mayer, C and Terentes-Printzios, D and Charlton, PH |
| Keywords: | Arterial stiffness, blood pressure, cardiovascular, central blood pressure, pulse wave velocity, machine learning, vascular ageing |
| Journal or Publication Title: | European Heart Journal - Digital Health |
| Publisher: | Oxford University Press |
| ISSN: | 2634-3916 |
| DOI / ID Number: | https://doi.org/10.1093/ehjdh/ztab089 |
| Copyright Information: | Copyright 2021 The Authors |
| Item Statistics: | View statistics for this item |
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