AUTHOR=Danilov Viacheslav V. , Klyshnikov Kirill Yu. , Gerget Olga M. , Skirnevsky Igor P. , Kutikhin Anton G. , Shilov Aleksandr A. , Ganyukov Vladimir I. , Ovcharenko Evgeny A. TITLE=Aortography Keypoint Tracking for Transcatheter Aortic Valve Implantation Based on Multi-Task Learning JOURNAL=Frontiers in Cardiovascular Medicine VOLUME=8 YEAR=2021 URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2021.697737 DOI=10.3389/fcvm.2021.697737 ISSN=2297-055X ABSTRACT=

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.