Artificially intelligent nasal perception for rapid sepsis diagnostics
Sepsis, a life-threatening disease caused by infection, presents a major global health challenge due to its high morbidity and mortality rates. A rapid and precise diagnosis of sepsis is essential for better patient outcomes. However, conventional diagnostic methods, such as bacterial cultures, are time-consuming and can delay sepsis diagnosis. Considering these, researchers investigated alternative techniques that detect volatile organic compounds (VOCs) produced by bacteria. In this study, we designed colorimetric gas sensor arrays, which change color upon interaction with biomarkers, offer a direct visual signal, and demonstrate high sensitivity and specificity in detecting sepsis-related VOCs. Furthermore, an artificial intelligence (AI) based algorithm, Rapid Sepsis Boosting (RSBoost), was employed as an analytical technique to enhance diagnostic accuracy (96.2%) in blood sample. This approach significantly improves the speed and accuracy of sepsis diagnostics within 24 h, holding great potential for transforming clinical diagnostics, saving lives, and reducing healthcare costs.
Authors
- Taehee Yoon (ORCID: https://orcid.org/0000-0002-4900-2555)
- Taehoon Lee (ORCID: https://orcid.org/0000-0002-9224-0866)
- Kyungyeon Lee (ORCID: https://orcid.org/0000-0001-9413-2925)
- Junwoo Lee (ORCID: https://orcid.org/0009-0005-9237-2403)
- Jaewoo Song (ORCID: https://orcid.org/0000-0002-1877-5731)
- Seongmin Ha (ORCID: https://orcid.org/0000-0002-0731-2301)
- Hyo‐Il Jung (ORCID: https://orcid.org/0000-0002-7474-9378)
- Chong‐Yun Kang (ORCID: https://orcid.org/0000-0002-4516-8160)
- Seon Yeong Park
- Sunyoung Park (ORCID: https://orcid.org/0000-0002-7826-9363)
- Jae Hyung Jang
- Heng Song (ORCID: https://orcid.org/0000-0002-0049-5053)
- Joonchul Shin (ORCID: https://orcid.org/0000-0003-4616-3879)
- Jin-Sang Kim
- Gwang Su Kim
- Sunghoon Hur
- Woong Heo
Institutions
- Georgia Institute of Technology (US)
- Yonsei University (KR)
- Korea University (KR)
- Korea Institute of Materials Science (KR)
- Daehang Hospital (KR)
- Ajou University (KR)
- Korea Institute of Science and Technology (KR)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2025-07-24
- DOI
- https://doi.org/10.1038/s41746-025-01851-4
- Citations
- 3
- Primary Topic
- Advanced Chemical Sensor Technologies
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- National Research Foundation
- Yonsei University
- Ministry of Trade, Industry and Energy
- Korea Institute of Science and Technology
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea