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오픈 소스 Netgen 라이브러리 기반의 관상동맥 형상 비정렬 격자 자동 생성 기법 연구
박상현(S.H. Park),장경식(K. Chang),이상욱(S.W. Lee),한동진(D. Han),장영걸(Y. Jang),장혁재(H. Chang) 한국전산유체공학회 2017 한국전산유체공학회지 Vol.22 No.2
In the present work, the algorithm for automatic tetrahedral mesh generation of the coronary artery geometry is proposed using open source software, Netgen library with LGPL license. Automatic processes include volume mesh generation and assignment of the boundary conditions, which are necessary for CFD simulation using in-house code. Automatic volume mesh generation is conducted based on Netgen library using STL-formatted file with surface elements information which is obtained through special treatment of coronary computed tomography angiography(CCTA) of the patient. For automatic assignments of the boundary conditions such as inlet, outlets and wall, the radius, the coordinate of center points and the normal vectors are used. With the arbitrary surface element, if two calculations are within the criteria; 1) the inner product with normal vector of selected surface element and one of inlet/outlet, 2)the distance between the selected element and the center point, the selected element is assigned inlet or outlet. Others which are not satisfied with above conditions are treated as the wall. The proposed algorithm is tested in the sample coronary artery. It is confirmed that generated volume mesh and boundary conditions are working well as the input file of in-house code.
In-Jeong Cho,Ji Min Sung,Hyeon Chang Kim,Sang Eun Lee,Myeong-Hun Chae,Maryam Kavousi,Oscar L. Rueda-Ochoa,M. Arfan Ikram,Oscar H. Franco,James K. Min,장혁재 대한심장학회 2020 Korean Circulation Journal Vol.50 No.1
Background and Objectives: We aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD), compared to Cox hazard regression. Methods: Two CVD prediction models were developed from National Health Insurance Service-Health Screening Cohort (NHIS-HEALS): a Cox regression model and a DL model. Performance of each model was assessed in the internal and 2 external validation cohorts in Koreans (National Health Insurance Service-National Sample Cohort; NHIS-NSC) and in Europeans (Rotterdam Study). A total of 412,030 adults in the NHIS-HEALS; 178,875 adults in the NHIS-NSC; and the 4,296 adults in Rotterdam Study were included. Results: Mean ages was 52 years (46% women) and there were 25,777 events (6.3%) in NHIS-HEALS during the follow-up. In internal validation, the DL approach demonstrated a C-statistic of 0.896 (95% confidence interval, 0.886–0.907) in men and 0.921 (0.908–0.934) in women and improved reclassification compared with Cox regression (net reclassification index [NRI], 24.8% in men, 29.0% in women). In external validation with NHIS-NSC, DL demonstrated a C-statistic of 0.868 (0.860–0.876) in men and 0.889 (0.876–0.898) in women, and improved reclassification compared with Cox regression (NRI, 24.9% in men, 26.2% in women). In external validation applied to the Rotterdam Study, DL demonstrated a C-statistic of 0.860 (0.824–0.897) in men and 0.867 (0.830–0.903) in women, and improved reclassification compared with Cox regression (NRI, 36.9% in men, 31.8% in women). Conclusions: A DL algorithm exhibited greater discriminative accuracy than Cox model approaches.