Aortic Annulus Detection Based on Deep Learning for Transcatheter Aortic Valve Replacement Using Cardiac Computed Tomography

Yongwon Cho, Soojung Park, Sung Ho Hwang, Minseok Ko, Do Sun Lim, Cheol Woong Yu, Seong Mi Park, Mi Na Kim, Yu Whan Oh, Guang Yang

Research output: Contribution to journalArticlepeer-review

Abstract

Background: To propose a deep learning architecture for automatically detecting the complex structure of the aortic annulus plane using cardiac computed tomography (CT) for transcatheter aortic valve replacement (TAVR). Methods: This study retrospectively reviewed consecutive patients who underwent TAVR between January 2017 and July 2020 at a tertiary medical center. Annulus Detection Permuted AdaIN network (ADPANet) based on a three-dimensional (3D) U-net architecture was developed to detect and localize the aortic annulus plane using cardiac CT. Patients (N = 72) who underwent TAVR between January 2017 and July 2020 at a tertiary medical center were enrolled. Ground truth using a limited dataset was delineated manually by three cardiac radiologists. Training, tuning, and testing sets (70:10:20) were used to build the deep learning model. The performance of ADPANet for detecting the aortic annulus plane was analyzed using the root mean square error (RMSE) and dice similarity coefficient (DSC). Results: In this study, the total dataset consisted of 72 selected scans from patients who underwent TAVR. The RMSE and DSC values for the aortic annulus plane using ADPANet were 55.078 ± 35.794 and 0.496 ± 0.217, respectively. Conclusion: Our deep learning framework was feasible to detect the 3D complex structure of the aortic annulus plane using cardiac CT for TAVR. The performance of our algorithms was higher than other convolutional neural networks.

Original languageEnglish
Article numbere306
JournalJournal of Korean medical science
Volume38
Issue number37
DOIs
Publication statusPublished - 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 The Korean Academy of Medical Sciences. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Keywords

  • Annulus Plane
  • Cardiac Image Analysis
  • Convolutional Neural Network
  • Deep Learning TAVR

ASJC Scopus subject areas

  • General Medicine

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