Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2
Aubin Samacoïts, Pattaraporn Nimsamer, Oraphan Mayuramart, Naphat Chantaravisoot, Pitchaya Sitthi‐Amorn, Chajchawan Nakhakes, Lumrung Luangkamchorn, Phongsakhon Tongcham, Ugo Zahm, Suchada Suphanpayak, Natta Padungwattanachoke, Nutcha Leelarthaphin, Hathaichanok Huayhongthong, Trairak Pisitkun, Sunchai Payungporn, Pimkhuan Hannanta‐anan
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.