Aortic Annulus Detection Based on Deep Learning for Transcatheter Aortic Valve Replacement Using Cardiac Computed Tomographyopen access
- Authors
- Cho, Yongwon; Park, Soojung; Hwang, Sung Ho; Ko, Minseok; Lim, Do-Sun; Yu, Cheol Woong; Park, Seong-Mi; Kim, Mi-Na; Oh, Yu-Whan; Yang, Guang
- Issue Date
- Aug-2023
- Publisher
- 대한의학회
- Keywords
- Annulus Plane; Cardiac Image Analysis; Convolutional Neural Network; Deep Learning TAVR
- Citation
- Journal of Korean Medical Science, v.38, no.37
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- Journal of Korean Medical Science
- Volume
- 38
- Number
- 37
- URI
- https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/64131
- DOI
- 10.3346/jkms.2023.38.e306
- ISSN
- 1011-8934
1598-6357
- 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. © 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.
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- Appears in
Collections - 2. Clinical Science > Department of Cardiology > 1. Journal Articles
- 2. Clinical Science > Department of Radiology > 1. Journal Articles
- 4. Research institute > Institute of Human Behavior and Genetics > 1. Journal Articles
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