Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network

Geon Kim; Daewoong Ahn; Minhee Kang; Jinho Park; DongHun Ryu; YoungJu Jo; Jinyeop Song; Jea Sung Ryu; Gunho Choi; Hyun Jung Chung; Kyuseok Kim; Doo Ryeon Chung; In Young Yoo; Hee Jae Huh; Hyun-seok Min; Nam Yong Lee; YongKeun Park

doi:10.1038/s41377-022-00881-x

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Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network

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- Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network
- Light: Science & Applications Vol. 11, Issue 7, Pages: 1595-1606(2022)
- Affiliations：
  
  1.Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
  2.KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea
  3.Tomocube Inc., Daejeon, 34109, Republic of Korea
  4.Smart Healthcare & Device Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
  5.Graduate School of Nanoscience and Technology, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
  6.Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
  7.Department of Emergency Medicine, Bundang CHA Hospital, Seongnam-si, Gyeonggi-Do, 13496, Korea
  8.Division of Infectious Diseases, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
  9.Department of Laboratory Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06591, Republic of Korea
  10.Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
  11.YoungJu Jo Present address: Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA
  12.Present address: Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Author bio：
  
  Nam Yong Lee (micro.lee@samsung.com)
  YongKeun Park (yk.park@kaist.ac.kr)
- Funds：
- DOI：10.1038/s41377-022-00881-x
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Kim, G. et al. Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network. Light: Science & Applications, 11, 1595-1606 (2022).
DOI：

Kim, G. et al. Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network. Light: Science & Applications, 11, 1595-1606 (2022). DOI： 10.1038/s41377-022-00881-x.

摘要

Abstract

The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections. Microbial infections are a major healthcare issue worldwide, as these widespread diseases often develop into deadly symptoms. While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection, this effective treatment is difficult to practice. The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification, which includes time-consuming sample growth. Here, we propose a microscopy-based framework that identifies the pathogen from single to few cells. Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network. We demonstrate the identification of 19 bacterial species that cause bloodstream infections, achieving an accuracy of 82.5% from an individual bacterial cell or cluster. This performance, comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample, underpins the effectiveness of our framework in clinical applications. Furthermore, our accuracy increases with multiple measurements, reaching 99.9% with seven different measurements of cells or clusters. We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.

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