전자통신동향분석 (Electronics and Telecommunications Trends)

한국전자통신연구원 (Electronics and Telecommunications Research Institute)
권호 표지
DOI QR코드

DOI QR Code

호흡기 감염병 진단 기술 동향

Trends in Diagnostic Technology for Respiratory Infectious Disease

  • 박정원 (진단치료기연구실) ;
  • 서홍석 (기업성장지원전략실) ;
  • 허철 (진단치료기연구실) ;
  • 박수준 (디지털바이오의료연구본부)
  • J.W. Park ;
  • H.-S. Seo ;
  • C. Huh ;
  • S.J. Park
  • 발행 : 2024.08.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The emergence and resurgence of novel respiratory infectious diseases since the turn of the millennium, including SARS, H1N1 flu, MERS, and COVID-19, have posed a significant global health threat. Efforts to combat these threats have involved various approaches, however, continued research and development are crucial to prepare for the possibility of emerging viruses and viral variants. Direct detection methods for viral pathogens include molecular diagnostic techniques and immunodiagnostic methods, while indirect diagnostic methods involve detecting changes in the condition of infected patients through imaging diagnostics, gas analysis, and biosignal measurement. Molecular diagnostic techniques, utilizing advanced technologies such as gene editing, are being developed to enable faster detection than traditional PCR methods, and research is underway to improve the efficiency of diagnostic devices. Diagnostic technologies for infectious diseases continue to evolve, and several key trends are expected to emerge in the future. Automation will facilitate widespread adoption of rapid and accurate diagnostics, portable diagnostic devices will enable immediate on-site diagnosis by healthcare professionals, and advancements in AI-based deep learning diagnostic models will enhance diagnostic accuracy.

키워드

과제정보

본 연구는 한국전자통신연구원 내부과제의 일환으로 수행되었음[22YR1210, 타액 기반 신변종 바이러스 현장형 신속 분자진단 시스템 개발].

참고문헌

  1. 전종홍, "COVID-19 이후, AI는 능동적 감염병 대응 도구로 발전할 수 있을까?," Future Horizon, 제47호, 2020, pp. 42-51.
  2. https://data.who.int/dashboards/covid19/cases?n=c
  3. 정은정, 한준, "코로나19의 경제.사회 영향 측정지표 구축방안 연구," 통계개발원, 2020년 연구보고서, 2021.
  4. 김종란, 여성율, "감염병 진단기술," KISTEP 기술동향 브리프, 2021-11호, 2021.
  5. 한국전자통신연구원, "타액기반 신변종 바이러스 현장형 신속분자진단 기술 개발," 최종보고서, 2022.
  6. 동아사이언스, "코로나19 감염 여부를 신속.정확하게 진단하는 새로운 방법," 2021, 4. 16., https://m.dongascience.com/news.php?idx=45755
  7. A. Basu et al., "Performance of Abbott ID Now COVID-19 rapid nucleic acid amplification test using nasopharyngeal swabs transported in viral transport media and dry nasal swabs in a New York City Academic Institution," J. Clin. Microbiol., vol. 58, 2020.
  8. J.S. Gootenberg et al., "Nucleic acid detection with CRISPR-Cas13a/C2c2," Science, vol. 356, no. 6336, 2017, pp. 438-442.
  9. M. Patchsung et al., "Clinical validation of a Cas13-based assay for the detection of SARS-CoV-2 RNA," Nat. Biomed. Eng., vol. 4, no. 12, 2020, pp. 1140-1149.
  10. Sherlock Biosciences, https://sherlock.bio/crispr-sarscov-2/
  11. 신화희, "신종 바이러스 감염병 팬데믹 대응을 위한 차세대 진단 기술," BT News, vol. 28, no. 1, 2021, pp. 46-53.
  12. 데일리 메드, "코로나19 진단, 유전자 증폭검사 vs 신속 면역항체검사," 2020. 4. 4., https://www.dailymedi.com/news/news_view.php?wr_id=854752
  13. World Health Organization, Laboratory testing for 2019 Novel Coronavirus (2019-nCoV) in Suspected Human Cases, 2020, pp. 1-7.
  14. J. Lei et al., "CT Imaging of the 2019 novel coronavirus (2019-nCoV) pneumonia," Radiology, vol. 295, no. 1, 2020.
  15. H. Shi et al., "Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: A descriptive study," Lancet Infect. Dis., vol. 20, no. 4, 2020, pp. 425-434.
  16. M. Chung et al., "CT imaging features of 2019 novel coronavirus (2019-nCoV)," Radiology, vol. 295, no. 1, 2020, pp. 202-207.
  17. Y. Wang et al., "Temporal changes of CT findings in 90 patients with COVID-19 pneumonia: A longitudinal study," Radiology, vol. 296, no. 2, 2020, pp. E55-E64.
  18. Y. Fang et al., "Sensitivity of chest CT for COVID-19: Comparison to RT-PCR," Radiology, vol. 296, no. 2, 2020, pp. E115-E117.