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2 March 2022 Detection and classification of SARS-CoV-2 through phase imaging with computational specificity (PICS)
Neha Goswami, Yuchen R. He, Yu-Heng Deng, Chamteut Oh, Sajeeb Chowdhury, Nahil Sobh, Enrique Valera, Rashid Bashir, Nahed Ismail, Hyunjoon Kong, Thanh H. Nguyen, Catherine Best-Popescu, Gabriel Popescu
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Proceedings Volume PC11970, Quantitative Phase Imaging VIII; PC119700J (2022) https://doi.org/10.1117/12.2616995
Event: SPIE BiOS, 2022, San Francisco, California, United States
Abstract
The COVID pandemic prompted the need for rapid detection of the SARS-CoV-2 virus and potentially other pathogens. In this study, we report a rapid, label-free optical detection method for SARS-CoV-2 that is aimed at detecting the virus in the patient’s breath condensates. We show in the published pre-clinical study that, through phase imaging with computational specificity (PICS), we can detect and classify SARS-CoV-2 versus other viruses (H1N1, HAdV and ZIKV) with 96% accuracy, within a minute after sample collection. PICS combines ultrasensitive quantitative phase imaging (QPI) with advanced deep-learning algorithms to detect and classify viral particles. The second stage of our project, currently under development, involves clinical validation of our proposed testing technique. Breath samples collected from patients in the clinic will be imaged with QPI and a U-Net model trained on the breath samples will identify the SARS-CoV-2 in the sample within a minute.
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Neha Goswami, Yuchen R. He, Yu-Heng Deng, Chamteut Oh, Sajeeb Chowdhury, Nahil Sobh, Enrique Valera, Rashid Bashir, Nahed Ismail, Hyunjoon Kong, Thanh H. Nguyen, Catherine Best-Popescu, and Gabriel Popescu "Detection and classification of SARS-CoV-2 through phase imaging with computational specificity (PICS)", Proc. SPIE PC11970, Quantitative Phase Imaging VIII, PC119700J (2 March 2022); https://doi.org/10.1117/12.2616995
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KEYWORDS
Phase imaging
Photonic integrated circuits
Biosensors
Pathogens
Plasmonic sensors
Plasmonics
Statistical modeling
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