An Artificial Intelligence-Assisted Portable Low-Cost Device for the Rapid Detection of SARS-CoV-2

Mukunthan Tharmakulasingam,Wamadeva Balachandran,Mohammed A Rohaim,Muhammad Munir,Nouman S Chaudhry,Aurore C Poirier,Manoharanehru Branavan,Anil Fernando,Roberto M La Ragione

doi:10.3390/electronics10172065

Abstract

An artificial intelligence-assisted low-cost portable device for the rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is presented here. This standalone temperature-controlled device houses tubes designed for conducting reverse transcription loop-mediated isothermal amplification (RT-LAMP) assays. Moreover, the device utilises tubes illuminated by LEDs, an in-built camera, and a small onboard computer with automated image acquisition and processing algorithms. This intelligent device significantly reduces the normal assay run time and removes the subjectivity associated with operator interpretation of colourimetric RT-LAMP results. To further improve this device’s usability, a mobile app has been integrated into the system to control the LAMP assay environment and to visually display the assay results by connecting the device to a smartphone via Bluetooth. This study was undertaken using ~5000 images produced from the ~200 LAMP amplification assays using the prototype device. Synthetic RNA and a small panel of positive and negative SARS-CoV-2 patient samples were assayed for this study. State-of-the-art image processing and artificial intelligence algorithms were applied to these images to analyse them and to select the most efficient algorithm. The template matching algorithm for image extraction and MobileNet CNN architecture for classification results provided 98.0% accuracy with an average run time of 20 min to confirm the endpoint result. Two working points were chosen based on the best compromise between sensitivity and specificity. The high sensitivity point has a sensitivity value of 99.12% and specificity value of 70.8%, while at the high specificity point, the sensitivity is 96.05% and specificity 93.59%. Furthermore, this device provides an efficient and cost-effective platform for non-health professionals to detect not only SARS-CoV-2 but also other pathogens in resource-limited laboratories, factories, airports, schools, universities, and homes.

Highlights

This article is an open access articleCOVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2 virus), has emerged as a highly infectious pandemic disease, rapidly spreading and severely compromising world health systems
This study describes the development of a low-cost rapid molecular diagnostic device integrated with a smartphone application for the rapid detection of SARS-CoV-2 using the artificial intelligence (AI) assisted reverse transcription loop-mediated isothermal amplification (RT-LAMP) method
This study is the first to demonstrate the implementation of unique image processing and AI-based algorithms in a portable, low-cost molecular diagnostic device for SARS-CoV2 (COVID-19) detection and to evaluate its performance

Summary

Introduction

COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2 virus), has emerged as a highly infectious pandemic disease, rapidly spreading and severely compromising world health systems. It causes severe acute respiratory syndrome and has resulted in significant disruption to the lives of the people and economies worldwide. The use of rapid molecular diagnostics has been significant in the improvement of human, plant, and animal health. It has great potential for improving, health, welfare, and economics if implemented efficiently and rapidly and integrated into a surveillance system. Standard molecular test methodologies use RT-qPCR, which has several drawbacks such as the need for expensive instruments, trained personnel, sophisticated facilities, and complex thermal cycling for the PCR reaction, and it is time-consuming [2,6]

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