scholarly journals Preliminary feasibility and hemodynamic performance of a newly-developed self-expanding bioprosthesis and 16-F delivery system in transcatheter aortic valve implantation in sheep

2012 ◽  
Vol 26 (3) ◽  
pp. 211-218 ◽  
Author(s):  
Cai
Keyword(s):  
Aortic Valve ◽  
Delivery System ◽  
Transcatheter Aortic Valve Implantation ◽  
Transcatheter Aortic Valve ◽  
Aortic Valve Implantation ◽  
Hemodynamic Performance ◽  
Valve Implantation

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.

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