首页> 中文期刊> 《光:科学与应用(英文版)》 >Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity

Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity

         

摘要

Efforts to mitigate the COVID-19 crisis revealed that fast,accurate,and scalable testing is crucial for curbing the current impact and that of future pandemics.We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification.An ultrasensitive interferometric method was used to image four virus types with nanoscale optical path-length sensitivity.Pairing these data with fluorescence images for ground truth,we trained semantic segmentation models based on U-Net,a particular type of convolutional neural network.The trained network was applied to classify the viruses from the interferometric images only,containing simultaneously SARS-CoV-2,H1N1(influenza-A virus),HAdV(adenovirus),and ZIKV(Zika virus).Remarkably,due to the nanoscale sensitivity in the input data,the neural network was able to identify SARS-CoV-2 vs.the other viruses with 96%accuracy.The inference time for each image is 60 ms,on a common graphic-processing unit.This approach of directly imaging unlabeled viral particles may provide an extremely fast test,of less than a minute per patient.As the imaging instrument operates on regular glass slides,we envision this method as potentially testing on patient breath condensates.The necessary high throughput can be achieved by translating concepts from digital pathology,where a microscope can scan hundreds of slides automatically.

著录项

  • 来源
    《光:科学与应用(英文版)》 |2021年第9期|1797-1808|共12页
  • 作者单位

    Department of Bioengineering;

    University of Illinois Urbana-Champaign;

    Urbana;

    Illinois;

    61801;

    USA;

    Beckman Institute of Advanced Science and Technology;

    University of Illinois Urbana-Champaign;

    Urbana;

    Illinois;

    61801;

    USA;

    Department of Electrical and Computer Engineering;

    University of Illinois Urbana-Champaign;

    Urbana;

    Illinois;

    61801;

    USA;

    Department of Chemical and Biomolecular Engineering;

    University of Illinois at Urbana-Champaign;

    Urbana;

    IL;

    61801;

    USA;

    Department of Civil and Environmental Engineering;

    University of Illinois at Urbana-Champaign;

    Urbana;

    IL;

    61801;

    USA;

    NCSA Center for Artificial Intelligence Innovation;

    University of Illinois at Urbana-Champaign;

    Urbana;

    IL;

    61801;

    USA;

    Holonyak Micro and Nanotechnology Laboratory;

    University of Illinois at Urbana-Champaign;

    Urbana;

    Illinois;

    61801;

    USA;

    Biomedical Research Center;

    Carle Foundation Hospital;

    509W University Ave.;

    Urbana;

    Illinois;

    61801;

    USA;

    Carle Illinois College of Medicine;

    807 South Wright St.;

    Urbana;

    Illinois;

    61801;

    USA;

    Mayo-Illinois Alliance for Technology Based Healthcare;

    Urbana;

    Illinois;

    61801;

    USA;

    Department of Pathology;

    College of Medicine;

    University of Illinois at Chicago;

    Chicago;

    IL;

    USA;

  • 原文格式 PDF
  • 正文语种 chi
  • 中图分类 TN9;
  • 关键词

    testing; neural; hundreds;

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