In-silico Analysis of Device-Related Thrombosis for Different Left Atrial Appendage Occluder Settings

2022 ◽  
pp. 160-168
Author(s):  
Eric Planas ◽  
Jordi Mill ◽  
Andy L. Olivares ◽  
Xabier Morales ◽  
Maria Isabel Pons ◽  
...  
Keyword(s):  
In Silico ◽  
Left Atrial ◽  
Left Atrial Appendage ◽  
In Silico Analysis ◽  
Atrial Appendage ◽  
Silico Analysis

scholarly journals In silico Optimization of Left Atrial Appendage Occluder Implantation Using Interactive and Modeling Tools

2019 ◽  
Vol 10 ◽  
Author(s):  
Ainhoa M. Aguado ◽  
Andy L. Olivares ◽  
Carlos Yagüe ◽  
Etelvino Silva ◽  
Marta Nuñez-García ◽  
...  
Keyword(s):  
In Silico ◽  
Left Atrial ◽  
Left Atrial Appendage ◽  
Atrial Appendage ◽  
Modeling Tools

scholarly journals Sensitivity Analysis of In Silico Fluid Simulations to Predict Thrombus Formation after Left Atrial Appendage Occlusion

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2304
Author(s):  
Jordi Mill ◽  
Victor Agudelo ◽  
Andy L. Olivares ◽  
Maria Isabel Pons ◽  
Etelvino Silva ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Boundary Conditions ◽  
In Silico ◽  
Left Atrial ◽  
Left Atrial Appendage ◽  
Thrombus Formation ◽  
Atrial Appendage ◽  
Patient Specific ◽  
Ground Truth Data ◽  
Left Atrial Appendage Occlusion

Atrial fibrillation (AF) is nowadays the most common human arrhythmia and it is considered a marker of an increased risk of embolic stroke. It is known that 99% of AF-related thrombi are generated in the left atrial appendage (LAA), an anatomical structure located within the left atrium (LA). Left atrial appendage occlusion (LAAO) has become a good alternative for nonvalvular AF patients with contraindications to anticoagulants. However, there is a non-negligible number of device-related thrombus (DRT) events, created next to the device surface. In silico fluid simulations can be a powerful tool to better understand the relation between LA anatomy, haemodynamics, and the process of thrombus formation. Despite the increasing literature in LA fluid modelling, a consensus has not been reached yet in the community on the optimal modelling choices and boundary conditions for generating realistic simulations. In this line, we have performed a sensitivity analysis of several boundary conditions scenarios, varying inlet/outlet and LA wall movement configurations, using patient-specific imaging data of six LAAO patients (three of them with DRT at follow-up). Mesh and cardiac cycle convergence were also analysed. The boundary conditions scenario that better predicted DRT cases had echocardiography-based velocities at the mitral valve outlet, a generic pressure wave from an AF patient at the pulmonary vein inlets, and a dynamic mesh approach for LA wall deformation, emphasizing the need for patient-specific data for realistic simulations. The obtained promising results need to be further validated with larger cohorts, ideally with ground truth data, but they already offer unique insights on thrombogenic risk in the left atria.

scholarly journals Deep Learning Framework for Real-Time Estimation of in-silico Thrombotic Risk Indices in the Left Atrial Appendage

2021 ◽  
Vol 12 ◽  
Author(s):  
Xabier Morales Ferez ◽  
Jordi Mill ◽  
Kristine Aavild Juhl ◽  
Cesar Acebes ◽  
Xavier Iriart ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Boundary Conditions ◽  
In Silico ◽  
Left Atrial ◽  
Left Atrial Appendage ◽  
Atrial Appendage ◽  
Superior Performance ◽  
Patient Specific ◽  
Cfd Simulations

Patient-specific computational fluid dynamics (CFD) simulations can provide invaluable insight into the interaction of left atrial appendage (LAA) morphology, hemodynamics, and the formation of thrombi in atrial fibrillation (AF) patients. Nonetheless, CFD solvers are notoriously time-consuming and computationally demanding, which has sparked an ever-growing body of literature aiming to develop surrogate models of fluid simulations based on neural networks. The present study aims at developing a deep learning (DL) framework capable of predicting the endothelial cell activation potential (ECAP), an in-silico index linked to the risk of thrombosis, typically derived from CFD simulations, solely from the patient-specific LAA morphology. To this end, a set of popular DL approaches were evaluated, including fully connected networks (FCN), convolutional neural networks (CNN), and geometric deep learning. While the latter directly operated over non-Euclidean domains, the FCN and CNN approaches required previous registration or 2D mapping of the input LAA mesh. First, the superior performance of the graph-based DL model was demonstrated in a dataset consisting of 256 synthetic and real LAA, where CFD simulations with simplified boundary conditions were run. Subsequently, the adaptability of the geometric DL model was further proven in a more realistic dataset of 114 cases, which included the complete patient-specific LA and CFD simulations with more complex boundary conditions. The resulting DL framework successfully predicted the overall distribution of the ECAP in both datasets, based solely on anatomical features, while reducing computational times by orders of magnitude compared to conventional CFD solvers.

A systematic review with in silico analysis on transcriptomic profile of gallbladder carcinoma

2020 ◽  
Vol 47 (6) ◽  
pp. 398-408
Author(s):  
Sonam Tulsyan ◽  
Showket Hussain ◽  
Balraj Mittal ◽  
Sundeep Singh Saluja ◽  
Pranay Tanwar ◽  
...  
Keyword(s):  
Systematic Review ◽  
Gallbladder Carcinoma ◽  
In Silico ◽  
In Silico Analysis ◽  
Transcriptomic Profile ◽  
Silico Analysis
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