Arterial pulse wave propagation across stenoses and aneurysms: assessment of one-dimensional simulations against three-dimensional simulations and<i>in vitro</i>measurements

Jin, Wen; Alastruey, Jordi

doi:10.1098/rsif.2020.0881

Cited by 21 publications

(23 citation statements)

References 71 publications

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“…Firstly, the computational model used in the present study is a one-dimensional model rather than a three-dimensional model that should be more accurate in simulating blood flow in the presence of an AAA. Nonetheless, a recent study has demonstrated that onedimensional modelling of the large arteries is accurate compared to three-dimensional modelling to simulate PW propagation across AAA [60]. Secondly, in patients with AAAs, calcification and intraluminal thrombus usually occur within the AAA, which change the stiffness (both the elastic modulus and wall thickness) of the AAA and significantly influence the distribution of AAA wall stresses [61][62][63].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Pulse Wave Analysis for Early Detection of Abdominal Aortic Aneurysms Using In Silico Pulse Waves

et al. 2021

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An abdominal aortic aneurysm (AAA) is usually asymptomatic until rupture, which is associated with extremely high mortality. Consequently, the early detection of AAAs is of paramount importance in reducing mortality; however, most AAAs are detected by medical imaging only incidentally. The aim of this study was to investigate the feasibility of machine learning-based pulse wave (PW) analysis for the early detection of AAAs using a database of in silico PWs. PWs in the large systemic arteries were simulated using one-dimensional blood flow modelling. A database of in silico PWs representative of subjects (aged 55, 65 and 75 years) with different AAA sizes was created by varying the AAA-related parameters with major impacts on PWs—identified by parameter sensitivity analysis—in an existing database of in silico PWs representative of subjects without AAAs. Then, a machine learning architecture for AAA detection was trained and tested using the new in silico PW database. The parameter sensitivity analysis revealed that the AAA maximum diameter and stiffness of the large systemic arteries were the dominant AAA-related biophysical properties considerably influencing the PWs. However, AAA detection by PW indexes was compromised by other non-AAA related cardiovascular parameters. The proposed machine learning model produced a sensitivity of 86.8 % and a specificity of 86.3 % in early detection of AAA from the photoplethysmogram PW signal measured in the digital artery with added random noise. The number of false positive and negative results increased with increasing age and decreasing AAA size, respectively. These findings suggest that machine learning-based PW analysis is a promising approach for AAA screening using PW signals acquired by wearable devices.

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Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Pulse Wave Analysis for Early Detection of Abdominal Aortic Aneurysms Using In Silico Pulse Waves

et al. 2021

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“… 2017 ; Chakshu et al. 2020 ; Jin and Alastruey 2021 ). The numerical implementation of the pulse-wave propagation model employed here is outlined in Jones et al.…”

Section: Methodsmentioning

confidence: 99%

“… 2018 ; Carson et al. 2019 ; Jin and Alastruey 2021 ) and aneurysms (Sazonov et al. 2017 ; Chakshu et al.…”

Section: Methodsmentioning

confidence: 99%

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

Jones

Parr²,

Nithiarasu³

et al. 2021

Biomech Model Mechanobiol

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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 $$F_1$$ F 1 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 $$F_1$$ F 1 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 $$F_1$$ F 1 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.

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“…As a second example, we considered a pathological configuration of aortic coarctation, that is a congenital defect consisting of a local narrowing of the aortic lumen (most commonly in the aortic arch). To this end, we considered an arterial segment of 126 mm endowed with a stenosis model [38], with a 30% stenosis halfway of its length. Terminal boundary conditions are given by an RCR Windkessel model.…”

Section: Stenotic Aortic Segmentmentioning

confidence: 99%

A coupling strategy for a 3D-1D model of the cardiovascular system to study the effects of pulse wave propagation on cardiac function

Caforio¹,

Augustin²,

Alastruey³

et al. 2021

Preprint

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The impact of increased stiffness and pulsatile load on the circulation and their influence on heart performance have been documented not only for cardiovascular events but also for ventricular dysfunctions. For this reason, computer models of cardiac electromechanics (EM) have to integrate effects of the circulatory system on heart function to be relevant for clinical applications. Currently it is not feasible to consider three-dimensional (3D) models of the entire circulation. Instead, simplified representations of the circulation are used, ensuring a satisfactory trade-off between accuracy and computational cost. In this work, we propose a novel and stable strategy to couple a 3D EM model of the heart to a one-dimensional (1D) model of blood flow in the arterial system. A personalised coupled 3D-1D model of LV and arterial system is built and used in a numerical benchmark to demonstrate robustness and accuracy of our scheme over a range of time steps. Validation of the coupled model is performed by investigating the coupled system's physiological response to variations in the arterial system affecting pulse wave propagation, comprising aortic stiffening, aortic stenosis or bifurcations causing wave reflections. Our results show that the coupled 3D-1D model is robust, stable and correctly replicates known physiology.

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Arterial pulse wave propagation across stenoses and aneurysms: assessment of one-dimensional simulations against three-dimensional simulations andin vitromeasurements

Cited by 21 publications

References 71 publications

Machine Learning-Based Pulse Wave Analysis for Early Detection of Abdominal Aortic Aneurysms Using In Silico Pulse Waves

Machine Learning-Based Pulse Wave Analysis for Early Detection of Abdominal Aortic Aneurysms Using In Silico Pulse Waves

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

A coupling strategy for a 3D-1D model of the cardiovascular system to study the effects of pulse wave propagation on cardiac function

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