Robust Finite Element Approaches to Systemic Circulation Using the Locally Conservative Galerkin (LCG) Method

In this paper novel finite element algorithms for robustly solving flow equations of asystemic circulation are presented and compared. A novel, Locally Conservative Galerkin (LCG)method is developed by adopting semi- and fully- implicit time discretisations to address the behaviourof blood flow in the human circulatory system. These techniques are efficient for nonlinearsystem of equations used in blood flow models and achieve rapid convergence. Severalcomparisons are made between the methods to demonstrate the validity and stability of the currentnumerical models to solve the blood flow characteristics in a human body

An AI based digital-twin for prioritising pneumonia patient treatment

A digital-twin based three-tiered system is proposed to prioritise patients for urgent intensive care and ventilator support. The deep learning methods are used to build patient-specific digital-twins to identify and prioritise critical cases amongst severe pneumonia patients. The three-tiered strategy is proposed to generate severity indices to: (1) identify urgent cases, (2) assign critical care and mechanical ventilation, and (3) discontinue mechanical ventilation and critical care at the optimal time. The severity indices calculated in the present study are the probability of death and the probability of requiring mechanical ventilation. These enable the generation of patient prioritisation lists and facilitates the smooth flow of patients in and out of Intensive Therapy Units (ITUs). The proposed digital-twin is built on pre-trained deep learning models using data from more than 1895 pneumonia patients. The severity indices calculated in the present study are assessed using the standard benchmark of Area Under Receiving Operating Characteristic Curve (AUROC). The results indicate that the ITU and mechanical ventilation can be prioritised correctly to an AUROC value as high as 0.89. This model may be employed in its current form to COVID-19 patients, but transfer learning with COVID-19 patient data will improve the predictions. The digital-twin model developed and tested is available via accompanying Supplemental material

Three-dimensional transverse vibration of microtubules

A three-dimensional (3D) transverse vibration was reported based on the molecular structural mechanics model for microtubules (MTs), where the bending axis of the cross section rotates in an anticlockwise direction and the adjacent half-waves oscillate in different planes. Herein, efforts were invested to capturing the physics behind the observed phenomenon and identifying the important factors that influence the rotation angle between two adjacent half waves. A close correlation was confirmed between the rotation of the oscillation planes and the helical structures of the MTs, showing that the 3D mode is a result of the helicity found in the MTs. Subsequently, the wave length-dependence and the boundary condition effects were also investigated for the 3D transverse vibration of the MTs. In addition, the vibration frequency was found to remain the same in the presence or absence of the bending axis rotation. This infers that the unique vibration mode is merely due to the bending axis rotation of the cross section, but no significant torsion occurs for the MTs

A Robust Finite Element Modeling Approach to Conjugate Heat Transfer in Flexible Elastic Tubes and Tube Networks

In this work, heat transfer between fluid flow in elastic tubes and external environment is modeled using a robust finite element approach. The transport of energy is coupled to fluid flow that is linked to the pressure and cross-sectional area variations of the tube. The novel model developed is applied to flow and heat transfer in elastic tubes with different geometric and material properties. The effects of reflections due to discontinuities and bifurcations in the tubes are also investigated. To determine the heat transport by conduction in the elastic walls, a radial heat conduction model is also incorporated. The coupled flow equations are solved using the locally conservative Galerkin finite element method, which provides an explicit element-wise conservation of fluxes. Several simulations are performed for different parametric variations to understand the relevant aspects of heat transfer in flexible elastic tubes. The results show that small temperature fluctuations are possible, inline with the pulsatile flow boundary conditions. It is also observed that increased flexibility of tubes leads to better heat transfer between the fluid and the wall. The results clearly indicate that any flow reflections also increase the heat transfer between the fluid and the wall

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

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

On the poro-elastic models for microvascular blood flow resistance: An in vitro validation

Nowadays, adequate and accurate representation of the microvascular flow resistance constitutes one of the major challenges in computational haemodynamic studies. In this work, a theoretical, porous media framework, ultimately designed for representing downstream resistance, is presented and compared against an in vitro experimental results. The resistor consists of a poro-elastic tube, with either a constant or variable porosity profile in space. The underlying physics, characterizing the fluid flow through the porous media, is analysed by considering flow variables at different network locations. Backward reflections, originated in the reservoir of the in vitro model, are accounted for through a reflection coefficient imposed as an outflow network condition. The simulation results are in good agreement with the measurements for both the homogenous and heterogeneous porosity conditions. In addition, the comparison allows identification of the range of values representing experimental reservoir reflection coefficients. The pressure drops across the heterogeneous porous media increases with respect to the simpler configuration, whilst flow remains almost unchanged. The effect of some fluid network features, such as tube Young’s modulus and fluid viscosity, on the theoretical results is also elucidated, providing a reference for the and simulation of different microvascular conditions

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 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

Perspectives on Sharing Models and Related Resources in Computational Biomechanics Research

The role of computational modeling for biomechanics will be increasingly prominent. In computational biomechanics, model sharing can facilitate assessment of reproducibility, and can provide an opportunity for repurposing and reuse, and a venue for medical training. The community's desire to investigate biological and biomechanical phenomena crossing multiple systems, scales, and physical domains, also motivates sharing of modeling resources as blending of models developed by domain experts is anticipated. The goal of this article is to understand current perspectives in the biomechanics community for the sharing of computational models and related resources. Opinions on opportunities, challenges, and pathways to model sharing, particularly as part of the scholarly publishing workflow, were sought. A synthesis of these opinion pieces indicates that the community recognizes the necessity and usefulness of model sharing. There is a strong will to facilitate model sharing and there are corresponding initiatives by the scientific journals. Outside the publishing enterprise, infrastructure to facilitate model sharing in biomechanics exists and simulation software developers are interested in accommodating the community's needs for sharing of modeling resources. Encouragement for the use of standardized markups, concerns related to quality assurance, acknowledgement of increased burden, and importance of stewardship of resources are noted. In the short-term, it is advisable that the community builds upon recent strategies and experiments with new pathways for continued demonstration of model sharing, its promotion, and its utility. Nonetheless, the need for a long term strategy to unify approaches in sharing computational models and related resources is acknowledged