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...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 10; pp. 2085 - 2115 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Oct2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152447251&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152447251 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Oct2021 vid: 59 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152447251 152123889 152447251 NLM34453662 152447251 10.1007/s11517-021-02424-9 NLM34453662 152447251 ppf: 2085 ppct: 30 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A proof of concept study for machine learning application to stenosis detection. aug: au: Jones, Gareth Parr, Jim Nithiarasu, Perumal Pant, Sanjay affil: Faculty of Science and Engineering, Swansea University, Swansea, UK sug: subj: Diagnostic Imaging Constriction, Pathologic Funding Source Human ab: 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. Graphical Abstract An overview of methodology fo the creation of virtual patients and their classification. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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