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

引用

Light(Science & Applications) 2022年第7期11卷 1595-1606页

作者： 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 Department of Physics Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea KAIST Institute for Health Science and Technology KAISTDaejeon 34141Republic of Korea Tomocube Inc. Daejeon 34109Republic of Korea Smart Healthcare&Device Research Center Samsung Medical CenterSungkyunkwan University School of MedicineSeoul 06351Republic of Korea Graduate School of Nanoscience and Technology Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Biological Sciences Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Emergency Medicine Bundang CHA HospitalSeongnam-siGyeonggi-Do 13496Korea Division of Infectious Diseases Department of Internal MedicineSamsung Medical CenterSungkyunkwan University School of MedicineSeoul 06351Republic of Korea Department of Laboratory Medicine Seoul St.Mary's HospitalCollege of MedicineThe Catholic University of KoreaSeoul 06591Republic of Korea Department of Laboratory Medicine and Genetics Samsung Medical CenterSungkyunkwan University School of MedicineSeoul 06351Republic of Korea Department of Applied Physics Stanford UniversityStanfordCA 94305USA Department of Physics Massachusetts Institute of TechnologyCambridgeMA 02139USA

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.

关键词： artificial neural exploit

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