Samacoits, Aubin, Nimsamer, Pattaraporn, Mayuramart, Oraphan, Chantaravisoot, Naphat, Sitthi-Amorn, Pitchaya, Nakhakes, Chajchawan, Luangkamchorn, Lumrung, Tongcham, Phongsakhon, Zahm, Ugo, Suphanpayak, Suchada et al (show 6 more authors)
(2021)
Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2.
ACS omega, 6 (4).
pp. 2727-2733.
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/μL 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Emerging Infectious Diseases, Biodefense, Prevention, Vaccine Related, Bioengineering, Lung, Infection, 3 Good Health and Well Being |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 08 Jul 2021 09:15 |
| Last Modified: | 17 Mar 2024 11:16 |
| DOI: | 10.1021/acsomega.0c04929 |
| Open Access URL: | https://doi.org/10.1021/acsomega.0c04929 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3129262 |
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