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A proof of concept study for machine learning application to stenosis detection
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8440304/ https://www.ncbi.nlm.nih.gov/pubmed/34453662 http://dx.doi.org/10.1007/s11517-021-02424-9 |
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author | Jones, Gareth Parr, Jim Nithiarasu, Perumal Pant, Sanjay |
author_facet | Jones, Gareth Parr, Jim Nithiarasu, Perumal Pant, Sanjay |
author_sort | Jones, Gareth |
collection | PubMed |
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. [Figure: see text] |
format | Online Article Text |
id | pubmed-8440304 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-84403042021-10-01 A proof of concept study for machine learning application to stenosis detection Jones, Gareth Parr, Jim Nithiarasu, Perumal Pant, Sanjay Med Biol Eng Comput Original Article 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. [Figure: see text] Springer Berlin Heidelberg 2021-08-28 2021 /pmc/articles/PMC8440304/ /pubmed/34453662 http://dx.doi.org/10.1007/s11517-021-02424-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Jones, Gareth Parr, Jim Nithiarasu, Perumal Pant, Sanjay A proof of concept study for machine learning application to stenosis detection |
title | 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_short | A proof of concept study for machine learning application to stenosis detection |
title_sort | proof of concept study for machine learning application to stenosis detection |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8440304/ https://www.ncbi.nlm.nih.gov/pubmed/34453662 http://dx.doi.org/10.1007/s11517-021-02424-9 |
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