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KMID : 0191120230380370306
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Journal of Korean Medical Science
2023 Volume.38 No. 37 p.306 ~ p.306
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Aortic Annulus Detection Based on Deep Learning for Transcatheter Aortic Valve Replacement Using Cardiac Computed Tomography
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Cho Yong-Won
Park Soo-Jung
Hwang Sung-Ho
Ko Min-Seok
Lim Do-Sun
Yu Cheol-Woong
Park Seong-Mi
Kim Mi-Na
Consuelo Suarez
Renz Cleve Vergara
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Abstract
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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.
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KEYWORD
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Annulus Plane, Cardiac Image Analysis, Convolutional Neural Network, Deep Learning TAVR
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