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

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 r...

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Autores principales: Bikia, Vasiliki, Fong, Terence, Climie, Rachel E, Bruno, Rosa-Maria, Hametner, Bernhard, Mayer, Christopher, Terentes-Printzios, Dimitrios, Charlton, Peter H
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7612526/
https://www.ncbi.nlm.nih.gov/pubmed/35316972
http://dx.doi.org/10.1093/ehjdh/ztab089
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author Bikia, Vasiliki
Fong, Terence
Climie, Rachel E
Bruno, Rosa-Maria
Hametner, Bernhard
Mayer, Christopher
Terentes-Printzios, Dimitrios
Charlton, Peter H
author_facet Bikia, Vasiliki
Fong, Terence
Climie, Rachel E
Bruno, Rosa-Maria
Hametner, Bernhard
Mayer, Christopher
Terentes-Printzios, Dimitrios
Charlton, Peter H
author_sort Bikia, Vasiliki
collection PubMed
description 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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spelling pubmed-76125262022-03-21 Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research Bikia, Vasiliki Fong, Terence Climie, Rachel E Bruno, Rosa-Maria Hametner, Bernhard Mayer, Christopher Terentes-Printzios, Dimitrios Charlton, Peter H Eur Heart J Digit Health Review 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. Oxford University Press 2021-10-18 /pmc/articles/PMC7612526/ /pubmed/35316972 http://dx.doi.org/10.1093/ehjdh/ztab089 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review
Bikia, Vasiliki
Fong, Terence
Climie, Rachel E
Bruno, Rosa-Maria
Hametner, Bernhard
Mayer, Christopher
Terentes-Printzios, Dimitrios
Charlton, Peter H
Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research
title Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research
title_full Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research
title_fullStr Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research
title_full_unstemmed Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research
title_short Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research
title_sort leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7612526/
https://www.ncbi.nlm.nih.gov/pubmed/35316972
http://dx.doi.org/10.1093/ehjdh/ztab089
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