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. 2021 Jan 20;6(4):2727-2733.
doi: 10.1021/acsomega.0c04929. eCollection 2021 Feb 2.

Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2

Aubin Samacoits  1 , Pattaraporn Nimsamer  2 , Oraphan Mayuramart  2 , Naphat Chantaravisoot  2   3 , Pitchaya Sitthi-Amorn  4 , Chajchawan Nakhakes  5 , Lumrung Luangkamchorn  6 , Phongsakhon Tongcham  6 , Ugo Zahm  1 , Suchada Suphanpayak  5 , Natta Padungwattanachoke  5 , Nutcha Leelarthaphin  5 , Hathaichanok Huayhongthong  5 , Trairak Pisitkun  2 , Sunchai Payungporn  2   3 , Pimkhuan Hannanta-Anan  6
Affiliations

Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2

Aubin Samacoits et al. ACS Omega. .

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.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Smartphone-based platform for the quantification of CRISPR diagnostic. (a) The overall workflow of the SARS-CoV-2 detection using the CRISPR diagnostic and the smartphone-based quantification platform. (b) Computer-aided design (CAD) renderings with an exploded view of (i) the device assembly and (ii) the actual images of the smartphone-based device for the fluorescence imaging of the CRISPR diagnostic assay. (c) Step-by-step summary of the custom software for the quantification of SARS-CoV-2.
Figure 2
Figure 2
Platform optimization for the detection of SARS-CoV-2. (a) Identification of optimal imaging parameters—phone model (left), excitation power (center), and exposure time (right)—for an improved detection sensitivity. (left) The detection sensitivities, represented by the regression slope, for smartphone model 1 and model 2 are 31.37 and 33.57 per 0.1 μM, respectively. (b) Signal quantification of the SARS-CoV-2 CRISPR diagnostic assay with a Zen dual-quenched probe (left) and a BHQ-quenched probe (center, right); fluorescence signals measured at 25 min timepoint (right). (c) Distribution of the fluorescence score for clinical samples plotted as a histogram. The red histogram corresponds to positive samples, whereas the blue one corresponds to negative samples (left). Receiver operating characteristics (ROC) curve from the fourfold cross-validation of a logistic regression trained on the fluorescence score displayed in the left panel. The blue curve is the mean ROC curve, and the gray area corresponds to confidence intervals of one standard deviation (middle). The right panel shows the distribution of the probability of samples being positive split into three groups according to their RT-qPCR Ct scores.
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