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Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis / Neeraj Kavan Chakshu; Igor Sazonov; Perumal Nithiarasu

Biomechanics and Modeling in Mechanobiology, Volume: 20, Issue: 2, Pages: 449 - 465

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

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Abstract

An exponential rise in patient data provides an excellent opportunity to improve the existing health care infrastructure. In the present work, a method to enable cardiovascular digital twin is proposed using inverse analysis. Conventionally, accurate analytical solutions for inverse analysis in line...

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Published in: Biomechanics and Modeling in Mechanobiology
ISSN: 1617-7959 1617-7940
Published: Springer Science and Business Media LLC 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa55503
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first_indexed 2020-10-23T08:52:40Z
last_indexed 2021-05-01T03:19:39Z
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spelling 2021-04-30T14:32:37.1201559 v2 55503 2020-10-23 Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis e21c85ee9062e9be0fff8ab9d77b14d7 Neeraj Kavan Chakshu Neeraj Kavan Chakshu true false 05a507952e26462561085fb6f62c8897 0000-0001-6685-2351 Igor Sazonov Igor Sazonov true false 3b28bf59358fc2b9bd9a46897dbfc92d 0000-0002-4901-2980 Perumal Nithiarasu Perumal Nithiarasu true false 2020-10-23 EEN An exponential rise in patient data provides an excellent opportunity to improve the existing health care infrastructure. In the present work, a method to enable cardiovascular digital twin is proposed using inverse analysis. Conventionally, accurate analytical solutions for inverse analysis in linear problems have been proposed and used. However, these methods fail or are not efficient for nonlinear systems, such as blood flow in the cardiovascular system (systemic circulation) that involves high degree of nonlinearity. To address this, a methodology for inverse analysis using recurrent neural network for the cardiovascular system is proposed in this work, using a virtual patient database. Blood pressure waveforms in various vessels of the body are inversely calculated with the help of long short-term memory (LSTM) cells by inputting pressure waveforms from three non-invasively accessible blood vessels (carotid, femoral and brachial arteries). The inverse analysis system built this way is applied to the detection of abdominal aortic aneurysm (AAA) and its severity using neural networks. Journal Article Biomechanics and Modeling in Mechanobiology 20 2 449 465 Springer Science and Business Media LLC 1617-7959 1617-7940 Inverse analysis, Deep learning, Digital twin technology, Systemic circulation, Blood flow, Aneurysm detection 1 4 2021 2021-04-01 10.1007/s10237-020-01393-6 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2021-04-30T14:32:37.1201559 2020-10-23T09:50:42.1107089 College of Engineering Engineering Neeraj Kavan Chakshu 1 Igor Sazonov 0000-0001-6685-2351 2 Perumal Nithiarasu 0000-0002-4901-2980 3 55503__18484__9c4dd770c66d410ab0e9e2408d990fef.pdf 55503.pdf 2020-10-23T09:52:15.1219243 Output 2016646 application/pdf Version of Record true © 2020 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 4.0 (CC BY) License true eng
title Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis
spellingShingle Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis
Neeraj Kavan, Chakshu
Igor, Sazonov
Perumal, Nithiarasu
title_short Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis
title_full Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis
title_fullStr Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis
title_full_unstemmed Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis
title_sort Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis
author_id_str_mv e21c85ee9062e9be0fff8ab9d77b14d7
05a507952e26462561085fb6f62c8897
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author_id_fullname_str_mv e21c85ee9062e9be0fff8ab9d77b14d7_***_Neeraj Kavan, Chakshu
05a507952e26462561085fb6f62c8897_***_Igor, Sazonov
3b28bf59358fc2b9bd9a46897dbfc92d_***_Perumal, Nithiarasu
author Neeraj Kavan, Chakshu
Igor, Sazonov
Perumal, Nithiarasu
author2 Neeraj Kavan Chakshu
Igor Sazonov
Perumal Nithiarasu
format Journal article
container_title Biomechanics and Modeling in Mechanobiology
container_volume 20
container_issue 2
container_start_page 449
publishDate 2021
institution Swansea University
issn 1617-7959
1617-7940
doi_str_mv 10.1007/s10237-020-01393-6
publisher Springer Science and Business Media LLC
college_str College of Engineering
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hierarchy_parent_title College of Engineering
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description An exponential rise in patient data provides an excellent opportunity to improve the existing health care infrastructure. In the present work, a method to enable cardiovascular digital twin is proposed using inverse analysis. Conventionally, accurate analytical solutions for inverse analysis in linear problems have been proposed and used. However, these methods fail or are not efficient for nonlinear systems, such as blood flow in the cardiovascular system (systemic circulation) that involves high degree of nonlinearity. To address this, a methodology for inverse analysis using recurrent neural network for the cardiovascular system is proposed in this work, using a virtual patient database. Blood pressure waveforms in various vessels of the body are inversely calculated with the help of long short-term memory (LSTM) cells by inputting pressure waveforms from three non-invasively accessible blood vessels (carotid, femoral and brachial arteries). The inverse analysis system built this way is applied to the detection of abdominal aortic aneurysm (AAA) and its severity using neural networks.
published_date 2021-04-01T04:19:34Z
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