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

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.