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

Neha Goswami; Yuchen R. He; Yu-Heng Deng; Chamteut Oh; Nahil Sobh; Enrique Valera; Rashid Bashir; Nahed Ismail; Hyunjoon Kong; Thanh H. Nguyen; Catherine Best-Popescu; Gabriel Popescu

doi:10.1038/s41377-021-00620-8

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

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- 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
- LSA 2021年10卷第9期页码：1797-1808
- Affiliations：
  
  1.Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, 61801, USA
  2.Beckman Institute of Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, 61801, USA
  3.Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois, 61801, USA
  4.Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
  5.Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
  6.NCSA Center for Artificial Intelligence Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
  7.Holonyak Micro and Nanotechnology Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
  8.Biomedical Research Center, Carle Foundation Hospital, 509W University Ave., Urbana, Illinois, 61801, USA
  9.Carle Illinois College of Medicine, 807 South Wright St., Urbana, Illinois, 61801, USA
  10.Mayo-Illinois Alliance for Technology Based Healthcare, Urbana, Illinois, 61801, USA
  11.Department of Pathology, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA
- Author bio：
  
  Gabriel Popescu(gpopescu@illinois.edu)
- Funds：
- DOI：10.1038/s41377-021-00620-8
  中图分类号：
- 纸质出版日期：2021-09-30，
  
  网络出版日期：2021-09-01，
  
  收稿日期：2021-03-10，
  
  修回日期：2021-08-03，
  
  录用日期：2021-08-18
- Accepted：
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Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity[J]. LSA, 2021,10(9):1797-1808.

Goswami N. H. et al. Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity. Light: Science & Applications, 10, 1797-1808 (2021).
Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity[J]. LSA, 2021,10(9):1797-1808. DOI： 10.1038/s41377-021-00620-8.

Goswami N. H. et al. Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity. Light: Science & Applications, 10, 1797-1808 (2021). DOI： 10.1038/s41377-021-00620-8.

摘要

Abstract

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.

关键词

Keywords

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