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

Vasiliki Bikia, Terence Fong, Rachel E. Climie, Rosa Maria Bruno, Bernhard Hametner, Christopher Clemens Mayer, Dimitrios Terentes-Printzios, Peter H. Charlton

    Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

    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.
    OriginalspracheEnglisch
    Seiten (von - bis)676-690
    Seitenumfang15
    FachzeitschriftEuropean Heart Journal - Digital Health
    Issue4
    DOIs
    PublikationsstatusVeröffentlicht - 2021

    Research Field

    • Ehemaliges Research Field - Health and Bioresources

    Schlagwörter

    • Arterial stiffness
    • Blood pressure
    • Cardiovascular
    • Central blood pressure
    • Pulse wave velocity
    • Machine learning

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