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

Aubin Samacoits; 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

doi:10.1021/acsomega.0c04929

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

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

Authors

Aubin Samacoits¹, Pattaraporn Nimsamer², Oraphan Mayuramart², Naphat Chantaravisoot^{2

3}, Pitchaya Sitthi-Amorn⁴, Chajchawan Nakhakes⁵, Lumrung Luangkamchorn⁶, Phongsakhon Tongcham⁶, Ugo Zahm¹, Suchada Suphanpayak⁵, Natta Padungwattanachoke⁵, Nutcha Leelarthaphin⁵, Hathaichanok Huayhongthong⁵, Trairak Pisitkun², Sunchai Payungporn^{2

3}, Pimkhuan Hannanta-Anan⁶

Affiliations

¹ Sertis Corporation, Bangkok 10110, Thailand.
² Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.
³ Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.
⁴ Department of Computer Engineering, Chulalongkorn University, Bangkok 10330, Thailand.
⁵ Department of Clinical Pathology and Medical Technology, Rajavithi Hospital, Bangkok 10400, Thailand.
⁶ School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.

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.