How to Facilitate Introduction of Delivery System in Transaortic Transcatheter Aortic Valve Implantation

2012 ◽  
Vol 28 (1) ◽  
pp. 14-15
Author(s):  
Vito Giovanni Ruggieri ◽  
Carine Ridard ◽  
Marc Bedossa ◽  
Xavier Beneux ◽  
Jean-Philippe Verhoye
Keyword(s):  
Aortic Valve ◽  
Delivery System ◽  
Transcatheter Aortic Valve Implantation ◽  
Transcatheter Aortic Valve ◽  
Aortic Valve Implantation ◽  
Valve Implantation

Transaortic Transcatheter Aortic Valve Implantation: Potential Issues Associated with the Use of the ASCENDRA Transapical Delivery System

2012 ◽  
Vol 27 (4) ◽  
pp. 438-440 ◽  
Author(s):  
Luis Nombela-Franco ◽  
Josep Rodés-Cabau ◽  
Daniel Doyle ◽  
Robert DeLarochellière ◽  
Marina Urena ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Delivery System ◽  
Transcatheter Aortic Valve Implantation ◽  
Transcatheter Aortic Valve ◽  
Aortic Valve Implantation ◽  
Valve Implantation

scholarly journals Aortography Keypoint Tracking for Transcatheter Aortic Valve Implantation Based on Multi-Task Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Viacheslav V. Danilov ◽  
Kirill Yu. Klyshnikov ◽  
Olga M. Gerget ◽  
Igor P. Skirnevsky ◽  
Anton G. Kutikhin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Aortic Valve ◽  
Delivery System ◽  
Transcatheter Aortic Valve Implantation ◽  
Anatomical Landmarks ◽  
Efficient Treatment ◽  
Task Learning ◽  
Transcatheter Aortic Valve ◽  
Aortic Valve Implantation ◽  
Valve Implantation

Currently, transcatheter aortic valve implantation (TAVI) represents the most efficient treatment option for patients with aortic stenosis, yet its clinical outcomes largely depend on the accuracy of valve positioning that is frequently complicated when routine imaging modalities are applied. Therefore, existing limitations of perioperative imaging underscore the need for the development of novel visual assistance systems enabling accurate procedures. In this paper, we propose an original multi-task learning-based algorithm for tracking the location of anatomical landmarks and labeling critical keypoints on both aortic valve and delivery system during TAVI. In order to optimize the speed and precision of labeling, we designed nine neural networks and then tested them to predict 11 keypoints of interest. These models were based on a variety of neural network architectures, namely MobileNet V2, ResNet V2, Inception V3, Inception ResNet V2 and EfficientNet B5. During training and validation, ResNet V2 and MobileNet V2 architectures showed the best prediction accuracy/time ratio, predicting keypoint labels and coordinates with 97/96% accuracy and 4.7/5.6% mean absolute error, respectively. Our study provides evidence that neural networks with these architectures are capable to perform real-time predictions of aortic valve and delivery system location, thereby contributing to the proper valve positioning during TAVI.

scholarly journals Simulation of Transcatheter Aortic Valve Implantation Procedure

2019 ◽  
Vol 14 (1) ◽  
pp. 204-219
Author(s):  
K.Yu. Klyshnikov ◽  
V.I. Ganyukov ◽  
A.V. Batranin ◽  
D.V. Nushtaev ◽  
E.A. Ovcharenko
Keyword(s):  
Aortic Valve ◽  
Delivery System ◽  
Transcatheter Aortic Valve Implantation ◽  
Research Work ◽  
Clinical Results ◽  
Patient Specific ◽  
Valve Prosthesis ◽  
Transcatheter Aortic Valve ◽  
Aortic Valve Implantation ◽  
Valve Implantation

The study is devoted to numerical modeling of transcatheter aortic valve implantation (TAVI) from the position of prognostic value in comparison with clinical data. The finite element method implemented in the Abaqus/CAE software and the reconstruction of three-dimensional models based on the computer microtomography of the CoreValve bioprosthesis of a size of 29 mm and the patient-specific data of functional studies (multispiral tomography) were used in the work. The study included three variations in the modeling of the aortic valve prosthesis procedure, which determine the level of detalization of the numerical experiment. All stages of the TAVI process were reproduced: the crimp of the prosthesis, the movement of the delivery system, the interaction of the guide - guidewire with the elements of the “prosthesis-root” of the aorta system, implantation itself. In silico experiment demonstrated significant quantitative and qualitative agreement with the data of intraoperative fluorography and computed tomography after the TAVI procedure. It is shown that the inclusion of additional elements – the guidewire and catheter of the delivery system into the “aortic root” has a positive effect on the convergence of the data with the clinical results. The analysis of the stress-strain state of the elements interacting in the experiment demonstrated a significant contribution to the analyzed parameters of the prosthetic motion stage along the guidewire as part of the delivery system catheter. Nevertheless, a comparison with the results of the clinical evaluation of the TAVI procedure revealed a number of differences in the response of the model of the bioprosthesis at the later stages of modeling, which requires further researches of a level of detalization. The approach is extremely promising both for practitioners and for research work of prosthetic designers, it can be applied in further R&D tasks.

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