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

Light: Science & Applications (Sep 2021)

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

Affiliations

Neha Goswami: Department of Bioengineering, University of Illinois Urbana-Champaign
Yuchen R. He: Beckman Institute of Advanced Science and Technology, University of Illinois Urbana-Champaign
Yu-Heng Deng: Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign
Chamteut Oh: Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign
Nahil Sobh: Beckman Institute of Advanced Science and Technology, University of Illinois Urbana-Champaign
Enrique Valera: Department of Bioengineering, University of Illinois Urbana-Champaign
Rashid Bashir: Department of Bioengineering, University of Illinois Urbana-Champaign
Nahed Ismail: Department of Pathology, College of Medicine, University of Illinois at Chicago
Hyunjoon Kong: Beckman Institute of Advanced Science and Technology, University of Illinois Urbana-Champaign
Thanh H. Nguyen: Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign
Catherine Best-Popescu: Department of Bioengineering, University of Illinois Urbana-Champaign
Gabriel Popescu: Department of Bioengineering, University of Illinois Urbana-Champaign

DOI: https://doi.org/10.1038/s41377-021-00620-8
Journal volume & issue: Vol. 10, no. 1
pp. 1 – 12

Abstract

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Rapid label-free detection of SARS-CoV-2 using phase imaging (spatial light-interference microscopy (SLIM)) with computational specificity. Different virus types captured in SLIM image are detected and classified by 2D U-Net.

Published in Light: Science & Applications

ISSN: 2047-7538 (Online)
Publisher: Nature Publishing Group
Country of publisher: United Kingdom
LCC subjects: Technology: Engineering (General). Civil engineering (General): Applied optics. Photonics; Science: Physics: Optics. Light
Website: https://www.nature.com/lsa/

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