Artificially intelligent nasal perception for rapid sepsis diagnostics.

Journal: NPJ digital medicine
Published Date: Jul 24, 2025

Abstract

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

  • Joonchul Shin
    School of Mechanical Engineering, Yonsei University, Seoul, Republic of Korea.
  • Gwang Su Kim
    Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea.
  • Seongmin Ha
    Institute of Biomedical Engineering Research, Kyungpook National University, 680, Gukchaebosang-ro, Jung-gu, Daegu 41944, Korea.
  • Taehee Yoon
    George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
  • Junwoo Lee
    Robotics Group, Korea Institute of Industrial Technology, Ansan 15588, Korea.
  • Taehoon Lee
    Department of Mechanical Engineering, Yonsei University, Seoul 03722, Korea.
  • Woong Heo
    School of Mechanical Engineering, Yonsei University, Seoul, Republic of Korea.
  • Kyungyeon Lee
    School of Mechanical Engineering, Yonsei University, Seoul, Republic of Korea.
  • Seong Jun Park
    Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, Korea.
  • Sunyoung Park
    Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.
  • Jaewoo Song
    Department of Laboratory Medicine, Yonsei University College of Medicine, Seodaemoon-gu, Seoul 03722, Republic of Korea.
  • Sunghoon Hur
    Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea.
  • Hyun-Cheol Song
    Materials Science and Engineering, Korea University, Seoul, Republic of Korea.
  • Ji-Soo Jang
    Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea. wkdwltn92@kist.re.kr.
  • Jin-Sang Kim
    Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea. jskim@kist.re.kr.
  • Hyo-Il Jung
    School of Mechanical Engineering, Yonsei University, Seoul, 03722, Republic of Korea. uridle7@yonsei.ac.kr.
  • Chong-Yun Kang
    Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea.

Keywords

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