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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
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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
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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...
| Published in: | International Journal for Numerical Methods in Biomedical Engineering |
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| ISSN: | 2040-7939 2040-7947 |
| Published: |
Wiley
2019
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa48157 |
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| 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. |
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| Keywords: |
biomechanical vibrations, blood flow, carotid stenoses, computer vision, digital twin, face video,systemic circulation |
| Issue: |
5 |
| Start Page: |
e3180 |