C‐THV measures of self‐expandable valve positioning and correlation with implant outcomes

Codner, Pablo; Lavi, Ifat; Malki, Guy; Vaknin‐Assa, Hana; Assali, Abid; Kornowski, Ran

doi:10.1002/ccd.25594

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Some interventional angiography systems integrate commercially available software to facilitate navigation during TAVI and reduce the risk of complications. Currently, such products have been developed by Philips (HeartNavigator) [4], Siemens Healthcare (syngo Aortic Valve Guide) [5], GE Healthcare (Innova HeartVision) [6], and Paieon Inc (C-THV) [7] and have been successfully implemented in clinical practice. Existing guidance systems align a three-dimensional anatomical model of the aortic root based on preoperative CT with live fluoroscopic images during valve positioning, ensuring optimal orientation of the angiography system and vascular access.…”

Section: Introductionmentioning

confidence: 99%

Application of Modern Object Tracking Technologies to the Task of Aortography Key Point Detection in Transcatheter Aortic Valve Implantation

Laptev,

Kochergin

2024

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Object detection, as one of the most fundamental and challenging problems in computer vision, has attracted much attention in recent years. Over the past two decades, we have witnessed the rapid technological evolution of object detection and its profound impact on the whole field of computer vision. In this paper, aortography key point detection approaches for transcatheter aortic valve implantation based on machine learning tools are discussed. The paper provides a description and analytical comparison of such popular methods as "object detection", "pose estimation". As a result of this study, a visual assessment system is proposed to facilitate the performance of the intervention procedure. The final accuracy of the proposed system reaches 79.3% with an analysis speed of 12 ms per image.

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Section: Introductionmentioning

confidence: 99%

Application of Modern Object Tracking Technologies to the Task of Aortography Key Point Detection in Transcatheter Aortic Valve Implantation

Laptev,

Kochergin

2024

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show abstract

“…Several interventional angiography systems integrate commercially available software to facilitate the navigation during TAVI for reducing the risk of complications. To date, such products have been developed by Philips (HeartNavigator), Siemens Healthcare (syngo Aortic Valve Guide), GE Healthcare (Innova HeartVision) ( 11 ), and Paieon Inc. (C-THV) ( 12 ) and were successfully introduced into clinical practice. The existing guidance systems align the computed tomography (CT)-based 3D anatomical model of the aortic root generated preoperatively and overlay it onto live fluoroscopy images during valve positioning, ensuring the optimal angiography system orientation and vascular access ( Figure 1 ).…”

Section: Introductionmentioning

confidence: 99%

“…The logical step forward is to design visual assistance systems providing an opportunity for the real-time tracking of keypoints and aortic root contour utilizing automated processing of the aortography images, regardless of the image acquisition equipment. For this task, neural networks capable of detecting regions of interest ( 12 , 14 ) on image series can be employed. Deep learning is currently becoming widespread in cardiovascular imaging ( 15 ) for examining aortic root hemodynamics ( 16 , 17 ), aortic dissection ( 18 ), aortic valve biomechanics ( 19 ), and coronary artery occlusion ( 20 ).…”

Section: Introductionmentioning

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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Adjuncts to transcatheter aortic valve implantation

McConkey

Arri

Joseph

et al. 2017

Expert Review of Cardiovascular Therapy

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The appreciable rise in percutaneous valve procedures has been pursued by a wave of development in advanced technology to help guide straightforward, streamlined and safe intervention. This review article aims to highlight the adjunctive devices, tools and techniques currently used in transcatheter aortic valve implantation procedures to avoid potential pitfalls. Areas covered: The software and devices featured here are at the forefront of technological advances, most of which are not yet in widespread use. These products have been discussed in national and international structural intervention conferences and the authors felt it important to showcase particularly well designed adjuncts that improve procedural efficacy and safety. Whilst vascular pre-closure systems are used routinely and are an integral part of these complex cardiovascular procedures, these have been well summarised elsewhere and are beyond the scope of this article. Expert commentary: The rising volume of patients with aortic stenosis who are treatable with TAVI means that this exponential increase in procedures must be accompanied by a steady decline in procedural complications. This section provides an overview of our current perspective, and what we feel the direction of travel will be.

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C‐THV measures of self‐expandable valve positioning and correlation with implant outcomes

Abstract: Self-expandable valve implants guided by C-THV navigation seems to be associated with more precise implants and lower risk for postprocedural electrical conduction defects, due to higher location in relation to the target line.

Cited by 4 publications

References 14 publications

Application of Modern Object Tracking Technologies to the Task of Aortography Key Point Detection in Transcatheter Aortic Valve Implantation

Application of Modern Object Tracking Technologies to the Task of Aortography Key Point Detection in Transcatheter Aortic Valve Implantation

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

Adjuncts to transcatheter aortic valve implantation

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