No Cover Image

Journal article 285 views 14 downloads

A semi‐active human digital twin model for detecting severity of carotid stenoses from head vibration—A coupled computational mechanics and computer vision method / Neeraj Kavan Chakshu; Jason Carson; Igor Sazonov; Perumal Nithiarasu

International Journal for Numerical Methods in Biomedical Engineering, Volume: 35, Issue: 5, Start page: e3180

Swansea University Authors: Neeraj Kavan, Chakshu, Jason, Carson, Igor, Sazonov, Perumal, Nithiarasu

  • 48157.pdf

    PDF | Version of Record

    Released under the terms of a Creative Commons Attribution License (CC-BY).

    Download (1.38MB)

Check full text

DOI (Published version): 10.1002/cnm.3180

Abstract

In this work we propose a methodology to detect the severity of carotid stenosis from a video of a human face with the help of a coupled blood flow and head vibration model. This semi‐active digital twin model is an attempt to link non‐invasive video of a patient face to the percentage of carotid oc...

Full description

Published in: International Journal for Numerical Methods in Biomedical Engineering
ISSN: 2040-7939 2040-7947
Published: Wiley 2019
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa48157
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: In this work we propose a methodology to detect the severity of carotid stenosis from a video of a human face with the help of a coupled blood flow and head vibration model. This semi‐active digital twin model is an attempt to link non‐invasive video of a patient face to the percentage of carotid occlusion. The pulsatile nature of blood flow through the carotid arteries induces a subtle head vibration. This vibration is a potential indicator of carotid stenosis severity and it is exploited in the present study. A head vibration model has been proposed in the present work that is linked to the forces generated by blood flow with or without occlusion. The model is used to generate a large number of virtual head vibration data for different degrees of occlusion. In order to determine the in vivo head vibration, a computer vision algorithm is adopted to use human face videos. The in vivo vibrations are compared against the virtual vibration data generated from the coupled computational blood flow/vibration model. A comparison of the in vivo vibration is made against the virtual data to find the best fit between in vivo and virtual data. The preliminary results on healthy subjects and a patient clearly indicate that the model is accurate and it possesses the potential for detecting approximate severity of carotid artery stenoses.
Keywords: biomechanical vibrations, blood flow, carotid stenoses, computer vision, digital twin, face video,systemic circulation
Issue: 5
Start Page: e3180