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Health level 7 fast healthcare interoperability resource (HL7 FHIR)를 통한 3차원 의료 영상의 교환

The medical 3-dimensional image exchange via health level 7 fast healthcare interoperability resource (HL7 FHIR)

  • 이정환 (부산대학교병원 신경외과) ;
  • 최병관 (부산대학교 의과대학 신경외과학교실) ;
  • 한인호 (부산대학교 의과대학 신경외과학교실)
  • Lee, Jung Hwan (Pusan National University Hospital) ;
  • Choi, Byung Kwan (Department of Neurosurgery, School of Medicine, Pusan National University) ;
  • Han, In Ho (Department of Neurosurgery, School of Medicine, Pusan National University)
  • 투고 : 2020.04.03
  • 심사 : 2020.06.20
  • 발행 : 2020.06.28

초록

의료 정보의 상호운용성 향상을 위해서 Health level 7은 의료 정보 교환을 위한 차세대 체계인 Fast health interoperability resource (FHIR)를 개발하였다. 그러나, 이를 이용하여 임상 정보를 포함한 3차원 의료 영상을 교환하려는 시도는 없어 새로운 방법을 제시하고자 한다. CT 영상에서 만들어진 3차원 의료 영상을 javascript object notation (JSON) 형식으로 전환하고, 임상 정보를 추가하였다. 우리는 시험용 FHIR 서버를 만들고, 클라이언트는 postman을 사용하였다. 생성된 JSON 파일은 body에 첨부하여 전송되었다. JSON 형식으로 전송된 3차원 의료 영상은 웹 브라우저를 통해서 볼 수 있었고, 원시 코드를 확인하여 동봉된 임상 정보를 볼 수 있었다. 우리는 3차원 의료 영상 교환을 최초로 시행하였다. 이 방법을 적용한 앱이나 FHIR 리소스 개발을 위해 추가적인 연구가 필요할 것이다.

For improving interoperability of medical information, health level 7 has initiated the development of a next-generation framework for the exchange of medical information called the Fast health interoperability resources (FHIR). However, there was no attempt to exchange the medical three-dimensional (3D) image with clinical data via FHIR. Thus, we designed a new method. The 3D image to be made from computed tomography was converted to the javascript object notation (JSON) file format, and clinical data was added. We made a test FHIR server, and the client used the postman. The JSON file was attached to the body, and was then transmitted. The transmitted 3D image could be seen through a web browser, and attached clinical data was identified in the source code. This is the first attempt to exchange the medical 3D image. Additional researches will be needed to develop applications or FHIR resources that apply this method.

키워드

참고문헌

  1. J. H. Lee, I. H. Han, B. K. Choi, K. H. Nam, D. H. Kim, C. S. Lee. (2017). Virtual preoperative simulation for excision of spinal tumors: surgeon processing of medical computer-assisted design software. Korean Journal of Spine, 14(4), 170-174 https://doi.org/10.14245/kjs.2017.14.4.170
  2. M. Jonathan, A. Feridun, H. Bronwyn, B. Kim. (2008). Preoperative visualization of neurovascular anatomy in trigeminal neuralgia. Journal of Neurosurgery, 108(3), 477-482 https://doi.org/10.3171/JNS/2008/108/3/0477
  3. P. S. Kim, C. H. Choi, I. H. Han, J. H. Lee, H. J. Choi, J. I. Lee. (2019). Obtaining informed consent using patient specific 3d printing cerebral aneurysm model. Journal of Korean Neurosurgical Society, 62(4), 398-404 https://doi.org/10.3340/jkns.2019.0092
  4. E. K. Park et al. (2016). Cranioplasty enhanced by three-dimensional printing: custom-made three-dimensional-printed titanium implants for skull defects. Journal of Craniofacial Surgery, 27(4), 943-949 https://doi.org/10.1097/SCS.0000000000002656
  5. K. C. Wong, S. M. Kumta, N. V. Geel, J. Demol. (2015). One-step reconstruction with a 3d-printed, biomechanically evaluated custom implant after complex pelvic tumor resection. Computer Aided Surgery, 20(1), 14-23 https://doi.org/10.3109/10929088.2015.1076039
  6. B. J. Kim, K. S. Hong, K. J. Park, D. H. Park, Y. G. Chung, S. H. Kang. (2012). Customized cranioplasty implants using three-dimensional printers and polymethyl-methacrylate casting. Journal of Korean Neurosurgical Society, 52(6), 541-546 https://doi.org/10.3340/jkns.2012.52.6.541
  7. J. H. Lee, J. H. Lee, W. Ryu, B. K. Choi, I. H. Han, C. M. Lee. (2019). Computer-based clinical coding activity analysis for neurosurgical terms. Yeungnam University Journal of Medicine, 36(3), 225-230. https://doi.org/10.12701/yujm.2019.00220
  8. D. Yoon, B. C. Chang, S. W. Kang, H. Bae, R. W. Park. (2012). Adoption of electronic health records in Korean tertiary teaching and general hospitals. International Journal of Medical Informatics, 81(3), 196-203. https://doi.org/10.1016/j.ijmedinf.2011.12.002
  9. M. V. Anderson, I. H. Kristensen, M. M. Larsen, C. H. Pederson, K. R. Goeg, L. B. Pape-Haugaard. (2017. May). Feasibility of representing a danish microbiology model using FHIR. Studies in Health Technology and Informatics, 235, 13-17. https://doi.org/10.3233/978-1-61499-753-5-13
  10. R. Saripalle, C. Runyan, M. Russell. (2019). Using HL7 FHIR to achieve interoperability in patient health record. Journal of Biomedical Informatics, 94, 103188. https://doi.org/10.1016/j.jbi.2019.103188
  11. C. Chroaki, F. Ploeg. (2016). Towards mhealth assessment guidelines for interoperability: HL7 FHIR. Studies in Health Technology and Informatics, 224, 164-169. https://doi.org/10.3233/978-1-61499-653-8-164
  12. M. Khalilia, M. Choi, A. Henderson, S. Iyengar, M. Braustein, J. Sun. (2015, November). Clinical predictive modeling development and deployment through FHIR web services. American Medical Informatics Association Annual Symposium Process 2015, pp. 717-726. San Francisco : Curran Associates, Inc.
  13. HL7 (2019). Resource index. HL7 FHIR Release 4. http:s//www.hl7.org/fhir/resourcelist.html
  14. K. J. Mortele, J. McTavish, P. R. Ros. (2002). Current techniques of computed tomography. helical CT, multidetector CT, and 3d reconstruction. Clinical Liver Disease, 6(1), 29-52. https://doi.org/10.1016/s1089-3261(03)00065-5
  15. C. Prieto et al. (2009). Image retake analysis in digital radiography using DICOM header information. Journal of Digital Imaging, 22(4), 393-399. https://doi.org/10.1007/s10278-008-9135-y