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Updated: May 6, 2026

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Vascular Age Evaluation Enhanced using Recurrence Plot Analysis and Convolutional Neural Networks: An in-Silico

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    Summary

    This study uses AI to estimate Vascular Age (VA) from arterial pulse waveforms, offering a new noninvasive method for cardiovascular risk assessment. This approach could improve patient care by providing a more personalized health evaluation.

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    Area of Science:

    • Cardiovascular Research
    • Biomedical Engineering
    • Artificial Intelligence in Medicine

    Background:

    • Aging is a primary nonreversible risk factor for cardiovascular disease.
    • Vascular Age (VA) is an emerging metric for assessing cardiovascular risk and overall health.
    • Current risk assessment methods may benefit from innovative, noninvasive approaches.

    Purpose of the Study:

    • To explore the use of a Convolutional Neural Network (CNN) for estimating Vascular Age (VA) groups.
    • To utilize Recurrence Plots for feature enhancement and visualization of Arterial Pulse Waveforms (APW).
    • To assess the potential of AI-driven analysis of APW for cardiovascular risk stratification.

    Main Methods:

    • Employed a Convolutional Neural Network (CNN) model.
    • Utilized Recurrence Plots for feature extraction and visualization from Arterial Pulse Waveforms (APW).
    • Data was sourced from an in-silico database of a one-dimensional cardiovascular model.

    Main Results:

    • The CNN achieved high accuracy (83% training, 81.3% testing) in estimating VA groups.
    • The model demonstrated strong performance with F1-scores of 83.3% (training) and 81.7% (testing).
    • Area Under the Curve (AUC) values of 0.96 (training) and 0.95 (testing) indicate robust predictive capability.

    Conclusions:

    • CNNs combined with Recurrence Plots show promise for noninvasive VA estimation from APW.
    • This methodology offers a potential new tool for cardiovascular risk assessment in clinical settings.
    • Further validation is required for real-world healthcare applications to enhance patient care and outcomes.