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您的检索式：作者名="DongHun Ryu"
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| 1 | Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network显示文摘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. | 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 | 2022 | Light(Science & Applications)2022，11，7： | 2 |
| 2 | Label-Free White Blood Cell Classification Using Refractive Index Tomography and Deep Learning显示文摘Objective and Impact Statement.We propose a rapid and accurate blood cell identification method exploiting deep learning and label-free refractive index(RI)tomography.Our computational approach that fully utilizes tomographic information of bone marrow(BM)white blood cell(WBC)enables us to not only classify the blood cells with deep learning but also quantitatively study their morphological and biochemical properties for hematology research.Introduction.Conventional methods for examining blood cells,such as blood smear analysis by medical professionals and fluorescence-activated cell sorting,require significant time,costs,and domain knowledge that could affect test results.While label-free imaging techniques that use a specimen’s intrinsic contrast(e.g.,multiphoton and Raman microscopy)have been used to characterize blood cells,their imaging procedures and instrumentations are relatively time-consuming and complex.Methods.The RI tomograms of the BM WBCs are acquired via Mach-Zehnder interferometer-based tomographic microscope and classified by a 3D convolutional neural network.We test our deep learning classifier for the four types of bone marrow WBC collected from healthy donors(n=10):monocyte,myelocyte,B lymphocyte,and T lymphocyte.The quantitative parameters of WBC are directly obtained from the tomograms.Results.Our results show>99%accuracy for the binary classification of myeloids and lymphoids and>96%accuracy for the four-type classification of B and T lymphocytes,monocyte,and myelocytes.The feature learning capability of our approach is visualized via an unsupervised dimension reduction technique.Conclusion.We envision that the proposed cell classification framework can be easily integrated into existing blood cell investigation workflows,providing cost-effective and rapid diagnosis for hematologic malignancy. | DongHun Ryu Jinho Kim Daejin Lim Hyun-Seok Min In Young Yoo Duck Cho YongKeun Park | 2021 | Biomedical Engineering Frontiers2021，2，1： | 0 |
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