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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 Orcid Logo, Igor Sazonov Orcid Logo, Perumal Nithiarasu Orcid Logo

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

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

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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...

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Published in: International Journal for Numerical Methods in Biomedical Engineering
ISSN: 2040-7939 2040-7947
Published: Wiley 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa48157
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spelling 2020-07-24T16:21:35.4289359 v2 48157 2019-01-14 A semi‐active human digital twin model for detecting severity of carotid stenoses from head vibration—A coupled computational mechanics and computer vision method e21c85ee9062e9be0fff8ab9d77b14d7 Neeraj Kavan Chakshu Neeraj Kavan Chakshu true false c1f2c28fbe6a41c5134b45abde5abb93 0000-0001-6634-9123 Jason Carson Jason Carson true false 05a507952e26462561085fb6f62c8897 0000-0001-6685-2351 Igor Sazonov Igor Sazonov true false 3b28bf59358fc2b9bd9a46897dbfc92d 0000-0002-4901-2980 Perumal Nithiarasu Perumal Nithiarasu true false 2019-01-14 GENG 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. Journal Article International Journal for Numerical Methods in Biomedical Engineering 35 5 e3180 Wiley 2040-7939 2040-7947 biomechanical vibrations, blood flow, carotid stenoses, computer vision, digital twin, face video,systemic circulation 6 5 2019 2019-05-06 10.1002/cnm.3180 COLLEGE NANME General Engineering COLLEGE CODE GENG Swansea University 2020-07-24T16:21:35.4289359 2019-01-14T09:47:00.8439959 Neeraj Kavan Chakshu 1 Jason Carson 0000-0001-6634-9123 2 Igor Sazonov 0000-0001-6685-2351 3 Perumal Nithiarasu 0000-0002-4901-2980 4 48157__17787__e5af120949964194b76e5e0289a80622.pdf 48157.pdf 2020-07-24T16:17:40.2399894 Output 1443310 application/pdf Version of Record true Released under the terms of a Creative Commons Attribution License (CC-BY). true eng http://creativecommons.org/licenses/by/4.0/
title A semi‐active human digital twin model for detecting severity of carotid stenoses from head vibration—A coupled computational mechanics and computer vision method
spellingShingle 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
title_short A semi‐active human digital twin model for detecting severity of carotid stenoses from head vibration—A coupled computational mechanics and computer vision method
title_full A semi‐active human digital twin model for detecting severity of carotid stenoses from head vibration—A coupled computational mechanics and computer vision method
title_fullStr A semi‐active human digital twin model for detecting severity of carotid stenoses from head vibration—A coupled computational mechanics and computer vision method
title_full_unstemmed A semi‐active human digital twin model for detecting severity of carotid stenoses from head vibration—A coupled computational mechanics and computer vision method
title_sort A semi‐active human digital twin model for detecting severity of carotid stenoses from head vibration—A coupled computational mechanics and computer vision method
author_id_str_mv e21c85ee9062e9be0fff8ab9d77b14d7
c1f2c28fbe6a41c5134b45abde5abb93
05a507952e26462561085fb6f62c8897
3b28bf59358fc2b9bd9a46897dbfc92d
author_id_fullname_str_mv e21c85ee9062e9be0fff8ab9d77b14d7_***_Neeraj Kavan Chakshu
c1f2c28fbe6a41c5134b45abde5abb93_***_Jason Carson
05a507952e26462561085fb6f62c8897_***_Igor Sazonov
3b28bf59358fc2b9bd9a46897dbfc92d_***_Perumal Nithiarasu
author Neeraj Kavan Chakshu
Jason Carson
Igor Sazonov
Perumal Nithiarasu
author2 Neeraj Kavan Chakshu
Jason Carson
Igor Sazonov
Perumal Nithiarasu
format Journal article
container_title International Journal for Numerical Methods in Biomedical Engineering
container_volume 35
container_issue 5
container_start_page e3180
publishDate 2019
institution Swansea University
issn 2040-7939
2040-7947
doi_str_mv 10.1002/cnm.3180
publisher Wiley
document_store_str 1
active_str 0
description 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.
published_date 2019-05-06T03:58:27Z
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