scholarly journals On the Role and Effects of Uncertainties in Cardiovascular in silico Analyses

2021 ◽  
Vol 3 ◽  
Author(s):  
Simona Celi ◽  
Emanuele Vignali ◽  
Katia Capellini ◽  
Emanuele Gasparotti
Keyword(s):  
Computational Analysis ◽  
Rigid Wall ◽  
Patient Specific ◽  
Computational Techniques ◽  
Error Sources ◽  
Moving Wall ◽  
Cardiovascular Hemodynamics ◽  
Computational Fluid Dynamics Cfd ◽  
Flow Pressure ◽  
Different Levels

The assessment of cardiovascular hemodynamics with computational techniques is establishing its fundamental contribution within the world of modern clinics. Great research interest was focused on the aortic vessel. The study of aortic flow, pressure, and stresses is at the basis of the understanding of complex pathologies such as aneurysms. Nevertheless, the computational approaches are still affected by sources of errors and uncertainties. These phenomena occur at different levels of the computational analysis, and they also strongly depend on the type of approach adopted. With the current study, the effect of error sources was characterized for an aortic case. In particular, the geometry of a patient-specific aorta structure was segmented at different phases of a cardiac cycle to be adopted in a computational analysis. Different levels of surface smoothing were imposed to define their influence on the numerical results. After this, three different simulation methods were imposed on the same geometry: a rigid wall computational fluid dynamics (CFD), a moving-wall CFD based on radial basis functions (RBF) CFD, and a fluid-structure interaction (FSI) simulation. The differences of the implemented methods were defined in terms of wall shear stress (WSS) analysis. In particular, for all the cases reported, the systolic WSS and the time-averaged WSS (TAWSS) were defined.

A Comprehensive Tool for Patient-Specific AAA Geometry and Biomechanics Assessment

2012 ◽  
Author(s):  
S. S. Raut ◽  
S. Chandra ◽  
J. Shum ◽  
P. Liu ◽  
E. S. Di Martino ◽  
...  
Keyword(s):  
Boundary Conditions ◽  
Cause Of Death ◽  
Geometric Modeling ◽  
Computational Analysis ◽  
The United States ◽  
Software Tools ◽  
Medical Image Segmentation ◽  
Patient Specific ◽  
Computational Techniques ◽  
Risk Of Rupture

Annual mortality from ruptured abdominal aortic aneurysm (AAA) in the United States alone is approximately 150,000, which is currently ranked as the 13th leading cause of death and the 10th leading cause of death in men over 55 years of age [1]. The vascular surgeon needs to weigh the risk of AAA rupture against the risk of surgical intervention to decide the best course of treatment. Several steps are involved when using computational techniques to evaluate risk of rupture [2], namely medical image segmentation, 3D reconstruction, finite element mesh generation, derivation of boundary conditions, specification of tissue material properties, etc. Currently, computational analysis of AAA biomechanics includes the use of multiple third-party commercial software tools to accomplish each of these steps, which makes its clinical implementation impractical, time-consuming and requiring to interface multiple software tools as this demands an engineering skill set. Additionally, the versatility of general purpose off-the-shelf software comes at the cost of simplifying assumptions regarding geometric modeling, limited user control and boundary conditions. This makes subsequent computational results vulnerable to inaccuracies. In this work, we describe the software tool AAAVASC, built on a MATLAB platform, with an integrated approach for image-based modeling and a novel pipeline that facilitates both geometry quantification and computational analysis of AAA biomechanics.

scholarly journals Morphological and Hemodynamic Changes during Cerebral Aneurysm Growth

2021 ◽  
Vol 11 (4) ◽  
pp. 520
Author(s):  
Emily R. Nordahl ◽  
Susheil Uthamaraj ◽  
Kendall D. Dennis ◽  
Alena Sejkorová ◽  
Aleš Hejčl ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Kinetic Energy ◽  
Swirling Flow ◽  
Morphological Changes ◽  
Cerebral Aneurysms ◽  
Patient Specific ◽  
Aneurysm Wall ◽  
Aneurysm Growth ◽  
Computational Fluid Dynamics Cfd

Computational fluid dynamics (CFD) has grown as a tool to help understand the hemodynamic properties related to the rupture of cerebral aneurysms. Few of these studies deal specifically with aneurysm growth and most only use a single time instance within the aneurysm growth history. The present retrospective study investigated four patient-specific aneurysms, once at initial diagnosis and then at follow-up, to analyze hemodynamic and morphological changes. Aneurysm geometries were segmented via the medical image processing software Mimics. The geometries were meshed and a computational fluid dynamics (CFD) analysis was performed using ANSYS. Results showed that major geometry bulk growth occurred in areas of low wall shear stress (WSS). Wall shape remodeling near neck impingement regions occurred in areas with large gradients of WSS and oscillatory shear index. This study found that growth occurred in areas where low WSS was accompanied by high velocity gradients between the aneurysm wall and large swirling flow structures. A new finding was that all cases showed an increase in kinetic energy from the first time point to the second, and this change in kinetic energy seems correlated to the change in aneurysm volume.

scholarly journals Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Gaoyang Li ◽  
Haoran Wang ◽  
Mingzi Zhang ◽  
Simon Tupin ◽  
Aike Qiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Artery Bypass ◽  
Computational Cost ◽  
Specific Model ◽  
Arterial System ◽  
Patient Specific ◽  
Cardiovascular Hemodynamics ◽  
Before And After ◽  
Deep Learning Network ◽  
Cfd Method

AbstractThe clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

scholarly journals Model Verification and Error Sensitivity of Turbulence-Related Tensor Characteristics in Pulsatile Blood Flow Simulations

Fluids ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 11
Author(s):  
Magnus Andersson ◽  
Matts Karlsson
Keyword(s):  
Blood Flow ◽  
Model Verification ◽  
Patient Specific ◽  
Flow Modeling ◽  
Spatiotemporal Resolution ◽  
Flow Simulations ◽  
Phase Averaging ◽  
Confidence Levels ◽  
Cardiovascular Flow ◽  
Computational Fluid Dynamics Cfd

Model verification, validation, and uncertainty quantification are essential procedures to estimate errors within cardiovascular flow modeling, where acceptable confidence levels are needed for clinical reliability. While more turbulent-like studies are frequently observed within the biofluid community, practical modeling guidelines are scarce. Verification procedures determine the agreement between the conceptual model and its numerical solution by comparing for example, discretization and phase-averaging-related errors of specific output parameters. This computational fluid dynamics (CFD) study presents a comprehensive and practical verification approach for pulsatile turbulent-like blood flow predictions by considering the amplitude and shape of the turbulence-related tensor field using anisotropic invariant mapping. These procedures were demonstrated by investigating the Reynolds stress tensor characteristics in a patient-specific aortic coarctation model, focusing on modeling-related errors associated with the spatiotemporal resolution and phase-averaging sampling size. Findings in this work suggest that attention should also be put on reducing phase-averaging related errors, as these could easily outweigh the errors associated with the spatiotemporal resolution when including too few cardiac cycles. Also, substantially more cycles are likely needed than typically reported for these flow regimes to sufficiently converge the phase-instant tensor characteristics. Here, higher degrees of active fluctuating directions, especially of lower amplitudes, appeared to be the most sensitive turbulence characteristics.

The Textual Evolution of the Ottoman Şeyhülislams’ Fetvas: A Cross-Corpora Computational Analysis

Der Islam ◽  
2021 ◽  
Vol 98 (2) ◽  
pp. 516-545
Author(s):  
Boğaç Ergene ◽  
Atabey Kaygun
Keyword(s):  
Seventeenth Century ◽  
Computational Analysis ◽  
Computational Techniques ◽  
Close Reading ◽  
Institutional Evolution ◽  
Multiple Forms ◽  
Term Evolution ◽  
Speed Up ◽  
Over Time

Abstract In this article, we use a mix of computational techniques to identify textual shifts in the Ottoman şeyhülislams’ fetvas between the sixteenth and twentieth centuries. Our analysis, supplemented by a close reading of these texts, indicates that the fetvas underwent multiple forms of transformation, a consequence of the institutional evolution of the şeyhülislam’s fetva office (fetvahane) that aimed to speed up and streamline the production of the fetvas: over time, the texts appropriated a more uniform character and came to contain shorter responses. In the compositions of the questions, we identified many “trigger terms” that facilitated reflexive responses independent of the fetvas’ jurisprudential contexts, a tendency that became stronger after the second half of the seventeenth century. In addition, we propose in the article a methodology that measures the relative strengths of textual and conceptual links among the fetva corpora of various Ottoman şeyhülislams. This analysis informs us about possible paths of long-term evolution of this genre of jurisprudential documents.

scholarly journals Identification of rupture locations in patient-specific abdominal aortic aneurysms using experimental and computational techniques

2010 ◽  
Vol 43 (7) ◽  
pp. 1408-1416 ◽  
Author(s):  
Barry J. Doyle ◽  
Aidan J. Cloonan ◽  
Michael T. Walsh ◽  
David A. Vorp ◽  
Timothy M. McGloughlin
Keyword(s):  
Abdominal Aortic Aneurysms ◽  
Aortic Aneurysms ◽  
Patient Specific ◽  
Computational Techniques ◽  
Abdominal Aortic

scholarly journals UCEasy: A software package for automating and simplifying the analysis of ultraconserved elements (UCEs)

2021 ◽  
Vol 9 ◽  
Author(s):  
Caio Ribeiro ◽  
Lucas Oliveira ◽  
Romina Batista ◽  
Marcos De Sousa
Keyword(s):  
Best Practices ◽  
Software Package ◽  
Phylogenetic Trees ◽  
Computational Analysis ◽  
Data Matrix ◽  
Command Line ◽  
Command Line Interface ◽  
Ultraconserved Elements ◽  
Research Software ◽  
Different Levels

The use of Ultraconserved Elements (UCEs) as genetic markers in phylogenomics has become popular and has provided promising results. Although UCE data can be easily obtained from targeted enriched sequencing, the protocol for in silico analysis of UCEs consist of the execution of heterogeneous and complex tools, a challenge for scientists without training in bioinformatics. Developing tools with the adoption of best practices in research software can lessen this problem by improving the execution of computational experiments, thus promoting better reproducibility. We present UCEasy, an easy-to-install and easy-to-use software package with a simple command line interface that facilitates the computational analysis of UCEs from sequencing samples, following the best practices of research software. UCEasy is a wrapper that standardises, automates and simplifies the quality control of raw reads, assembly and extraction and alignment of UCEs, generating at the end a data matrix with different levels of completeness that can be used to infer phylogenetic trees. We demonstrate the functionalities of UCEasy by reproducing the published results of phylogenomic studies of the bird genus Turdus (Aves) and of Adephaga families (Coleoptera) containing genomic datasets to efficiently extract UCEs.

scholarly journals Variant Selection and Interpretation: An Example of Modified VarSome Classifier of ACMG Guidelines in the Diagnostic Setting

Genes ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1885
Author(s):  
Francesca Cristofoli ◽  
Elisa Sorrentino ◽  
Giulia Guerri ◽  
Roberta Miotto ◽  
Roberta Romanelli ◽  
...  
Keyword(s):  
Medical Genetics ◽  
Patient Specific ◽  
Specific Information ◽  
Variant Selection ◽  
Variant Classification ◽  
Variant Interpretation ◽  
Bioinformatics Pipeline ◽  
Different Levels ◽  
Diagnostic Setting

Variant interpretation is challenging as it involves combining different levels of evidence in order to evaluate the role of a specific variant in the context of a patient’s disease. Many in-depth refinements followed the original 2015 American College of Medical Genetics (ACMG) guidelines to overcome subjective interpretation of criteria and classification inconsistencies. Here, we developed an ACMG-based classifier that retrieves information for variant interpretation from the VarSome Stable-API environment and allows molecular geneticists involved in clinical reporting to introduce the necessary changes to criterion strength and to add or exclude criteria assigned automatically, ultimately leading to the final variant classification. We also developed a modified ACMG checklist to assist molecular geneticists in adjusting criterion strength and in adding literature-retrieved or patient-specific information, when available. The proposed classifier is an example of integration of automation and human expertise in variant curation, while maintaining the laboratory analytical workflow and the established bioinformatics pipeline.

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