Visual and Robotic Guidance Systems for Transcatheter Implantation of Heart Value Prostheses

Овчаренко, Е. А.; Savrasov, G. V.; Klyshnikov, K. Yu.

doi:10.1007/s10527-017-9672-0

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…Another promising research area is the development of a feedback loop for robotic-assisted TAVI systems that have been designed for experimental purposes ( 10 , 34 ). The main concept of this approach is the use of manipulators compatible with the commercial TAVI systems that would deliver and position valves instead of interventional cardiologists, who will then monitor and control the work of the robotic assistant.…”

Section: Discussionmentioning

confidence: 99%

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

Danilov

Klyshnikov

Gerget

et al. 2021

Front. Cardiovasc. Med.

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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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Section: Discussionmentioning

confidence: 99%

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

Danilov

Klyshnikov

Gerget

et al. 2021

Front. Cardiovasc. Med.

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New Energy Station Monitoring Equipment Rule Verification Model Based on Data Envelopment Analysis

Zhu

et al. 2021

IOP Conf. Ser.: Earth Environ. Sci.

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According to the functional and performance requirements of the new energy station monitoring equipment, an operating rule verification model based on data envelopment analysis is proposed, and the online automatic discovery of equipment and safe operation rules for key links are designed. The equipment adopts data envelopment analysis method to read real-time data of new energy station monitoring equipment, optimizes the automatic discovery function of key sections, and tests the operating parameters of wind turbines and wind farms online through remote testers, formulates monitoring rules, and forms data requirements, Use algorithms to acquire and process data, and build models. Realize the discrete processing and rule mining of key links and safety operating procedures to ensure the safe operation of new energy station monitoring equipment. Finally, the experimental results show that the equipment can quickly and automatically generate new energy station monitoring equipment safe operation knowledge and improve new energy Safety of station monitoring equipment.

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Development of a Biological Tissue Relaxation Frequency Analyzer During Electrosurgical Action

Karpuhin

Maksimov

Derevyankin

et al. 2021

2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)

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Visual and Robotic Guidance Systems for Transcatheter Implantation of Heart Value Prostheses

Cited by 3 publications

References 22 publications

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

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

New Energy Station Monitoring Equipment Rule Verification Model Based on Data Envelopment Analysis

Development of a Biological Tissue Relaxation Frequency Analyzer During Electrosurgical Action

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