1.Stented Aortic Graft Insertion in an Infrarenal Abdominal Aortic Aneurysm as Performed by Cardiovascular Surgeons: Report of 3 cases.
Euisuk CHUNG ; Cheong LIM ; Yongwon SEONG ; Jin Ho CHOI ; Kay Hyun PARK ; Woo Young CHUNG
The Korean Journal of Thoracic and Cardiovascular Surgery 2008;41(3):377-380
Abdominal aortic aneurysm has traditionally been treated by open repair. Aortic endovascular stent grafting has recently been introduced as a new modality. We report here on three cases of endovascular stent grafting that were performed by cardiovascular surgeons for the treatment of abdominal aortic aneurysm in the high risk patients with multiple comorbidities such as old age, hypertension, renal failure, cerebrovascular accident and immobility.
Aneurysm
;
Aortic Aneurysm
;
Aortic Aneurysm, Abdominal
;
Comorbidity
;
Humans
;
Hypertension, Renal
;
Stents
;
Stroke
;
Transplants
2.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
Journal of Korean Medical Science 2023;38(37):e306-
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.