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유학제,염윤진,박수완,이정문,장문정,김유중,김종호,박현준,박재형,주형준 대한의료정보학회 2023 Healthcare Informatics Research Vol.29 No.2
Objectives: Electrocardiography (ECG)-based diagnosis by experts cannot maintain uniform quality because individual differencesmay occur. Previous public databases can be used for clinical studies, but there is no common standard that wouldallow databases to be combined. For this reason, it is difficult to conduct research that derives results by combining databases. Recent commercial ECG machines offer diagnoses similar to those of a physician. Therefore, the purpose of this study was toconstruct a standardized ECG database using computerized diagnoses. Methods: The constructed database was standardizedusing Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Observational Medical Outcomes Partnership–common data model (OMOP-CDM), and data were then categorized into 10 groups based on the Minnesota classification. In addition, to extract high-quality waveforms, poor-quality ECGs were removed, and database bias was minimizedby extracting at least 2,000 cases for each group. To check database quality, the difference in baseline displacement accordingto whether poor ECGs were removed was analyzed, and the usefulness of the database was verified with seven classificationmodels using waveforms. Results: The standardized KURIAS-ECG database consists of high-quality ECGs from 13,862 patients,with about 20,000 data points, making it possible to obtain more than 2,000 for each Minnesota classification. An artificialintelligence classification model using the data extracted through SNOMED-CT showed an average accuracy of 88.03%. Conclusions: The KURIAS-ECG database contains standardized ECG data extracted from various machines. The proposedprotocol should promote cardiovascular disease research using big data and artificial intelligence.
뉴로내비게이션 시스템 표면정합에 대한 병변 정합 오차의 회전적 특성 분석: 팬텀 연구
박현준,문정환,유학제,신기영,심태용,Park, Hyun-Joon,Mun, Joung Hwan,Yoo, Hakje,Shin, Ki-Young,Sim, Taeyong 대한의용생체공학회 2016 의공학회지 Vol.37 No.2
In this study, we investigated the rotational characteristics which were comprised of directionality and linearity of target registration error (TRE) as a study in advance to enhance the accuracy of contour-based registration in neuronavigation. For the experiment, two rigid head phantoms that have different faces with specially designed target frame fixed inside of the phantoms were used. Three-dimensional coordinates of facial surface point cloud and target point of the phantoms were acquired using computed tomography (CT) and 3D scanner. Iterative closest point (ICP) method was used for registration of two different point cloud and the directionality and linearity of TRE in overall head were calculated by using 3D position of targets after registration. As a result, it was represented that TRE had consistent direction in overall head region and was increased in linear fashion as distance from facial surface, but did not show high linearity. These results indicated that it is possible for decrease TRE by controlling orientation of facial surface point cloud acquired from scanner, and the prediction of TRE from surface registration error can decrease the registration accuracy in lesion. In the further studies, we have to develop the contour-based registration method for improvement of accuracy by considering rotational characteristics of TRE.