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A proof of concept study for machine learning application to stenosis detection

Gareth Jones, Jim Parr, Perumal Nithiarasu Orcid Logo, Sanjay Pant Orcid Logo

Medical & Biological Engineering & Computing, Volume: 59, Issue: 10, Pages: 2085 - 2114

Swansea University Authors: Perumal Nithiarasu Orcid Logo, Sanjay Pant Orcid Logo

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DOI (Published version): 10.1007/s11517-021-02424-9

Abstract

This proof of concept (PoC) assesses the ability of machine learning (ML) classifiers to predict the presence of a stenosis in a three vessel arterial system consisting of the abdominal aorta bifurcating into the two common iliacs. A virtual patient database (VPD) is created using one-dimensional pu...

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Published in: Medical & Biological Engineering & Computing
ISSN: 0140-0118 1741-0444
Published: Springer Science and Business Media LLC 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa57550
first_indexed 2021-08-06T10:38:05Z
last_indexed 2022-06-24T03:15:09Z
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spelling 2022-06-23T14:59:33.4452173 v2 57550 2021-08-06 A proof of concept study for machine learning application to stenosis detection 3b28bf59358fc2b9bd9a46897dbfc92d 0000-0002-4901-2980 Perumal Nithiarasu Perumal Nithiarasu true false 43b388e955511a9d1b86b863c2018a9f 0000-0002-2081-308X Sanjay Pant Sanjay Pant true false 2021-08-06 ACEM This proof of concept (PoC) assesses the ability of machine learning (ML) classifiers to predict the presence of a stenosis in a three vessel arterial system consisting of the abdominal aorta bifurcating into the two common iliacs. A virtual patient database (VPD) is created using one-dimensional pulse wave propagation model of haemodynamics. Four different machine learning (ML) methods are used to train and test a series of classifiers—both binary and multiclass—to distinguish between healthy and unhealthy virtual patients (VPs) using different combinations of pressure and flow-rate measurements. It is found that the ML classifiers achieve specificities larger than 80% and sensitivities ranging from 50 to 75%. The most balanced classifier also achieves an area under the receiver operative characteristic curve of 0.75, outperforming approximately 20 methods used in clinical practice, and thus placing the method as moderately accurate. Other important observations from this study are that (i) few measurements can provide similar classification accuracies compared to the case when more/all the measurements are used; (ii) some measurements are more informative than others for classification; and (iii) a modification of standard methods can result in detection of not only the presence of stenosis, but also the stenosed vessel. Journal Article Medical & Biological Engineering & Computing 59 10 2085 2114 Springer Science and Business Media LLC 0140-0118 1741-0444 Arterial disease diagnosis; Machine learning; Virtual patient database; Pulse wave haemodynamics 1 10 2021 2021-10-01 10.1007/s11517-021-02424-9 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University SU Library paid the OA fee (TA Institutional Deal) This work is supported by an Engineering and Physical Science Research Council studentship ref. EP/N509553/1 and an Engineering and Physical Science Research Council grant ref. EP/R010811/1. 2022-06-23T14:59:33.4452173 2021-08-06T11:35:10.7757120 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Gareth Jones 1 Jim Parr 2 Perumal Nithiarasu 0000-0002-4901-2980 3 Sanjay Pant 0000-0002-2081-308X 4 57550__20806__9d93273590cb4e6b88c3a314af231149.pdf 57550.pdf 2021-09-09T14:39:20.9334476 Output 1984800 application/pdf Version of Record true © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/
title A proof of concept study for machine learning application to stenosis detection
spellingShingle A proof of concept study for machine learning application to stenosis detection
Perumal Nithiarasu
Sanjay Pant
title_short A proof of concept study for machine learning application to stenosis detection
title_full A proof of concept study for machine learning application to stenosis detection
title_fullStr A proof of concept study for machine learning application to stenosis detection
title_full_unstemmed A proof of concept study for machine learning application to stenosis detection
title_sort A proof of concept study for machine learning application to stenosis detection
author_id_str_mv 3b28bf59358fc2b9bd9a46897dbfc92d
43b388e955511a9d1b86b863c2018a9f
author_id_fullname_str_mv 3b28bf59358fc2b9bd9a46897dbfc92d_***_Perumal Nithiarasu
43b388e955511a9d1b86b863c2018a9f_***_Sanjay Pant
author Perumal Nithiarasu
Sanjay Pant
author2 Gareth Jones
Jim Parr
Perumal Nithiarasu
Sanjay Pant
format Journal article
container_title Medical & Biological Engineering & Computing
container_volume 59
container_issue 10
container_start_page 2085
publishDate 2021
institution Swansea University
issn 0140-0118
1741-0444
doi_str_mv 10.1007/s11517-021-02424-9
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
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description This proof of concept (PoC) assesses the ability of machine learning (ML) classifiers to predict the presence of a stenosis in a three vessel arterial system consisting of the abdominal aorta bifurcating into the two common iliacs. A virtual patient database (VPD) is created using one-dimensional pulse wave propagation model of haemodynamics. Four different machine learning (ML) methods are used to train and test a series of classifiers—both binary and multiclass—to distinguish between healthy and unhealthy virtual patients (VPs) using different combinations of pressure and flow-rate measurements. It is found that the ML classifiers achieve specificities larger than 80% and sensitivities ranging from 50 to 75%. The most balanced classifier also achieves an area under the receiver operative characteristic curve of 0.75, outperforming approximately 20 methods used in clinical practice, and thus placing the method as moderately accurate. Other important observations from this study are that (i) few measurements can provide similar classification accuracies compared to the case when more/all the measurements are used; (ii) some measurements are more informative than others for classification; and (iii) a modification of standard methods can result in detection of not only the presence of stenosis, but also the stenosed vessel.
published_date 2021-10-01T20:12:36Z
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