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교량 구조물 안전점검 자동화를 위한 정합 이미지 생성 시스템
김석진 ( Seokjin Kim ),이성원 ( Sungwon Lee ),전민건 ( Mingeon Jeon ),김수종 ( Sujong Kim ),서동만 ( Dongmahn Seo ) 한국정보처리학회 2019 한국정보처리학회 학술대회논문집 Vol.26 No.2
고도성장 당시 건축되었던 사회기반시설들은 노후화로 인한 안전사고의 위험성이 부각되고 있다. 사회기반시설 중 교량의 경우 건축방식, 기후에 따른 안전점검 간 제약사항이 생긴다. 본 논문에서는 드론을 통해 점검이 필요한 교량을 촬영한다. 교량의 촬영 데이터를 정합 이미지로 생성하여 교량 내 유지보수를 위한 교량 모니터링 시스템을 제안한다.
Shin, Seo Jeong,You, Seng Chan,Park, Yu Rang,Roh, Jin,Kim, Jang-Hee,Haam, Seokjin,Reich, Christian G,Blacketer, Clair,Son, Dae-Soon,Oh, Seungbin,Park, Rae Woong JMIR Publications 2019 Journal of medical Internet research Vol.21 No.3
<P><B>Background</B></P><P>Clinical sequencing data should be shared in order to achieve the sufficient scale and diversity required to provide strong evidence for improving patient care. A distributed research network allows researchers to share this evidence rather than the patient-level data across centers, thereby avoiding privacy issues. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) used in distributed research networks has low coverage of sequencing data and does not reflect the latest trends of precision medicine.</P><P><B>Objective</B></P><P>The aim of this study was to develop and evaluate the feasibility of a genomic CDM (G-CDM), as an extension of the OMOP-CDM, for application of genomic data in clinical practice.</P><P><B>Methods</B></P><P>Existing genomic data models and sequencing reports were reviewed to extend the OMOP-CDM to cover genomic data. The Human Genome Organisation Gene Nomenclature Committee and Human Genome Variation Society nomenclature were adopted to standardize the terminology in the model. Sequencing data of 114 and 1060 patients with lung cancer were obtained from the Ajou University School of Medicine database of Ajou University Hospital and The Cancer Genome Atlas, respectively, which were transformed to a format appropriate for the G-CDM. The data were compared with respect to gene name, variant type, and actionable mutations.</P><P><B>Results</B></P><P>The G-CDM was extended into four tables linked to tables of the OMOP-CDM. Upon comparison with The Cancer Genome Atlas data, a clinically actionable mutation, p.Leu858Arg, in the <I>EGFR</I> gene was 6.64 times more frequent in the Ajou University School of Medicine database, while the p.Gly12Xaa mutation in the <I>KRAS</I> gene was 2.02 times more frequent in The Cancer Genome Atlas dataset. The data-exploring tool GeneProfiler was further developed to conduct descriptive analyses automatically using the G-CDM, which provides the proportions of genes, variant types, and actionable mutations. GeneProfiler also allows for querying the specific gene name and Human Genome Variation Society nomenclature to calculate the proportion of patients with a given mutation.</P><P><B>Conclusions</B></P><P>We developed the G-CDM for effective integration of genomic data with standardized clinical data, allowing for data sharing across institutes. The feasibility of the G-CDM was validated by assessing the differences in data characteristics between two different genomic databases through the proposed data-exploring tool GeneProfiler. The G-CDM may facilitate analyses of interoperating clinical and genomic datasets across multiple institutions, minimizing privacy issues and enabling researchers to better understand the characteristics of patients and promote personalized medicine in clinical practice.</P>