Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2
- 2 February 2021
- journal article
- research article
- Published by American Chemical Society (ACS) in ACS Omega
- Vol. 6 (4), 2727-2733
- https://doi.org/10.1021/acsomega.0c04929
Abstract
Rapid, accurate, and low-cost detection of SARS-CoV-2 is crucial to contain the transmission of COVID-19. Here, we present a cost-effective smartphone-based device coupled with machine learning-driven software that evaluates the fluorescence signals of the CRISPR diagnostic of SARS-CoV-2. The device consists of a three-dimensional (3D)-printed housing and low-cost optic components that allow excitation of fluorescent reporters and selective transmission of the fluorescence emission to a smartphone. Custom software equipped with a binary classification model has been developed to quantify the acquired fluorescence images and determine the presence of the virus. Our detection system has a limit of detection (LoD) of 6.25 RNA copies/mu L on laboratory samples and produces a test accuracy of 95% and sensitivity of 97% on 96 nasopharyngeal swab samples with transmissible viral loads. Our quantitative fluorescence score shows a strong correlation with the quantitative reverse transcription polymerase chain reaction (RT-qPCR) Ct values, offering valuable information of the viral load and, therefore, presenting an important advantage over nonquantitative readouts.Funding Information
- Faculty of Medicine, Chulalongkorn University (RA(P0)002/63)
- Innovation Fund to fight against COVID-19
- King Mongkut?s Institute of Technology Ladkrabang (KREF186312)
- School of Engineering, King Mongkut?s Institute of Technology Ladkrabang (2564-02-01-002)
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