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Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database

G. Jones, J. Parr, Perumal Nithiarasu Orcid Logo, Sanjay Pant Orcid Logo

Biomechanics and Modeling in Mechanobiology, Volume: 20, Issue: 6, Pages: 2097 - 2146

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

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DOI (Published version): 10.1007/s10237-021-01497-7

Abstract

This study presents an application of machine learning (ML) methods for detecting the presence of stenoses and aneurysms in the human arterial system. Four major forms of arterial disease—carotid artery stenosis (CAS), subclavian artery stenosis (SAS), peripheral arterial disease (PAD), and abdomina...

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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/cronfa57307
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spelling 2022-06-23T14:56:52.0125725 v2 57307 2021-07-12 Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database 3b28bf59358fc2b9bd9a46897dbfc92d 0000-0002-4901-2980 Perumal Nithiarasu Perumal Nithiarasu true false 43b388e955511a9d1b86b863c2018a9f 0000-0002-2081-308X Sanjay Pant Sanjay Pant true false 2021-07-12 ACEM This study presents an application of machine learning (ML) methods for detecting the presence of stenoses and aneurysms in the human arterial system. Four major forms of arterial disease—carotid artery stenosis (CAS), subclavian artery stenosis (SAS), peripheral arterial disease (PAD), and abdominal aortic aneurysms (AAA)—are considered. The ML methods are trained and tested on a physiologically realistic virtual patient database (VPD) containing 28,868 healthy subjects, adapted from the authors previous work and augmented to include disease. It is found that the tree-based methods of Random Forest and Gradient Boosting outperform other approaches. The performance of ML methods is quantified through the F1 score and computation of sensitivities and specificities. When using six haemodynamic measurements (pressure in the common carotid, brachial, and radial arteries; and flow-rate in the common carotid, brachial, and femoral arteries), it is found that maximum F1 scores larger than 0.9 are achieved for CAS and PAD, larger than 0.85 for SAS, and larger than 0.98 for both low- and high-severity AAAs. Corresponding sensitivities and specificities are larger than 90% for CAS and PAD, larger than 85% for SAS, and larger than 98% for both low- and high-severity AAAs. When reducing the number of measurements, performance is degraded by less than 5% when three measurements are used, and less than 10% when only two measurements are used for classification. For AAA, it is shown that F1 scores larger than 0.85 and corresponding sensitivities and specificities larger than 85% are achievable when using only a single measurement. The results are encouraging to pursue AAA monitoring and screening through wearable devices which can reliably measure pressure or flow-rates. Journal Article Biomechanics and Modeling in Mechanobiology 20 6 2097 2146 Springer Science and Business Media LLC 1617-7959 1617-7940 Virtual patients; Stenosis; Aneurysm; Pulse wave haemodynamics; Screening; Machine learning 1 12 2021 2021-12-01 10.1007/s10237-021-01497-7 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 EPSRC studentship ref. EP/N509553/1 and an EPSRC grant ref. EP/R010811/1. 2022-06-23T14:56:52.0125725 2021-07-12T13:18:46.5307670 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering G. Jones 1 J. Parr 2 Perumal Nithiarasu 0000-0002-4901-2980 3 Sanjay Pant 0000-0002-2081-308X 4 57307__20526__d0ae8982775e43798ceeb05fbfdd5c58.pdf 57307.pdf 2021-08-03T16:09:26.8703898 Output 2647455 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 Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database
spellingShingle Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database
Perumal Nithiarasu
Sanjay Pant
title_short Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database
title_full Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database
title_fullStr Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database
title_full_unstemmed Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database
title_sort Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database
author_id_str_mv 3b28bf59358fc2b9bd9a46897dbfc92d
43b388e955511a9d1b86b863c2018a9f
author_id_fullname_str_mv 3b28bf59358fc2b9bd9a46897dbfc92d_***_Perumal Nithiarasu
43b388e955511a9d1b86b863c2018a9f_***_Sanjay Pant
author Perumal Nithiarasu
Sanjay Pant
author2 G. Jones
J. Parr
Perumal Nithiarasu
Sanjay Pant
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description This study presents an application of machine learning (ML) methods for detecting the presence of stenoses and aneurysms in the human arterial system. Four major forms of arterial disease—carotid artery stenosis (CAS), subclavian artery stenosis (SAS), peripheral arterial disease (PAD), and abdominal aortic aneurysms (AAA)—are considered. The ML methods are trained and tested on a physiologically realistic virtual patient database (VPD) containing 28,868 healthy subjects, adapted from the authors previous work and augmented to include disease. It is found that the tree-based methods of Random Forest and Gradient Boosting outperform other approaches. The performance of ML methods is quantified through the F1 score and computation of sensitivities and specificities. When using six haemodynamic measurements (pressure in the common carotid, brachial, and radial arteries; and flow-rate in the common carotid, brachial, and femoral arteries), it is found that maximum F1 scores larger than 0.9 are achieved for CAS and PAD, larger than 0.85 for SAS, and larger than 0.98 for both low- and high-severity AAAs. Corresponding sensitivities and specificities are larger than 90% for CAS and PAD, larger than 85% for SAS, and larger than 98% for both low- and high-severity AAAs. When reducing the number of measurements, performance is degraded by less than 5% when three measurements are used, and less than 10% when only two measurements are used for classification. For AAA, it is shown that F1 scores larger than 0.85 and corresponding sensitivities and specificities larger than 85% are achievable when using only a single measurement. The results are encouraging to pursue AAA monitoring and screening through wearable devices which can reliably measure pressure or flow-rates.
published_date 2021-12-01T08:03:03Z
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