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

Autor:	Danilov VV; Research Laboratory for Processing and Analysis of Big Data, Tomsk Polytechnic University, Tomsk, Russia., Klyshnikov KY; Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia., Gerget OM; Research Laboratory for Processing and Analysis of Big Data, Tomsk Polytechnic University, Tomsk, Russia., Skirnevsky IP; Research Laboratory for Processing and Analysis of Big Data, Tomsk Polytechnic University, Tomsk, Russia., Kutikhin AG; Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia., Shilov AA; Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia., Ganyukov VI; Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia., Ovcharenko EA; Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia.
Jazyk:	angličtina
Zdroj:	Frontiers in cardiovascular medicine [Front Cardiovasc Med] 2021 Jul 19; Vol. 8, pp. 697737. Date of Electronic Publication: 2021 Jul 19 (Print Publication: 2021).
DOI:	10.3389/fcvm.2021.697737
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2021 Danilov, Klyshnikov, Gerget, Skirnevsky, Kutikhin, Shilov, Ganyukov and Ovcharenko.)
Databáze:	MEDLINE
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