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AI-LAMP for rapid detection of SARS-CoV-2

Though qPCR remains the gold standard for the diagnosis of SARS-CoV-2, loop mediated isothermal amplification (LAMP) assays have been demonstrated to produce diagnostic results with increased sensitivity and specificity. Further, its ability to tolerate PCR inhibitors eliminates the need for laborious RNA extraction and purification methodologies. Several platforms capable of performing LAMP assays in the field of diagnostics have been demonstrated in the past. However, most platforms employed fluorescence detection with integrated optical units or a smartphone dock to achieve detection. Similarly, for colorimetric LAMP assays, smartphone cameras or user interpretation of the colour change was used to achieve detection. The fully integrated real-time fluorescence-based platforms were expensive, and the smartphone-based platforms were only designed for specific smartphone models. Therefore, to fulfil the need for a standalone colorimetric isothermal nucleic acid amplification platform, we have developed an ultra-low-cost molecular diagnostic device with an integrated single-board computer, imaging camera, artificial intelligence-based image processing algorithm and mobile app.

During the early stages of the lock-down period in April, a consortium of academics from Brunel University London, University of Lancaster and the University of Surrey has been working on a Newton Fund grant administered by the BBSRC to develop a low-cost, handheld, rapid detection device for molecular detection of poultry pathogens for in farm use in the Philippines. The technique employed in the device was colorimetric AI-LAMP. It was collectively decided to repurpose the project to produce devices for detecting SARS-CoV-2. In the middle of April, a team led by Professor Balachandran of Brunel University London rapidly assembled 10 devices to perform preliminary tests of SARS-CoV-2 using the CL3 laboratories in Lancaster and Surrey. These devices are shown in Figure 1.

AI-Lamp

These prototype devices were built with many off-the-shelf electronic components and custom flexible resistive heating elements and specially designed aluminium heating blocks. Raspberry Pi (RPi) was used to control the device. The Python-based control software was used to control the heating, the image progression of the LAMP assay and store the ‘time-lapse’ images and temperature data within a specified folder. The user can initiate a test by either connecting to a screen via the HDMI port or by simply pairing the device with the mobile app via Bluetooth and selecting the required diagnostic assay. We are also developing a novel sample preparation cartridge for easy extraction of RNA from swab samples.

Industrial Collaboration: Based on this successful outcome we partnered with Vidiia Ltd (www.vidiia.co.uk) to manufacture devices with improved firmware and software to comply with CE marking criteria stipulated by regulatory bodies. The company has improved the ergonomics of the existing prototype to enhance functionality and safety features. The CAD has been optimized to incorporate additional features; including thermal management and design for manufacturing principles. The device manufactured by Viddia is called “Virus Hunter 6 - VH6” and is shown in Figure 2. It includes a communication module to enable it to communicate via a mobile network. Further improvement of the AI-based image processing algorithm was also implemented on VH6.

Virus Hunter 6 – VH6

The device (VH6) can be used to obtain SARS-CoV-2 test results quickly and with great ease. Test preparation is simple and fast. Test preparation time is under 10 minutes including swab collection, the RT-LAMP assay takes 20 minutes to reach endpoint result, and total test time is therefore 30 minutes. It works by linking a test kit to a test device along with the use of a smart device app for android or iOS. The smart app registers patients for swab collection and managers the entire process, thus ensuring tests are traced and secure. The smart test app also walks the operator through the test at every stage. The VH6 can run up to six individual tests simultaneously. The results are interpreted by the in-built camera and AI algorithms and displayed through Vidiia’s innovative smart app. The VH6 has been CE IVD and MHRA registered, and ready for commercialisation.


Meet the Principal Investigator(s) for the project

Professor Wamadeva Balachandran - Professor Balachandran is Professor of Electronic Systems and served as Head of Department of Systems Engineering at Brunel University London, UK, from 1999 to 2004. Before joining Brunel University London in 1995, he was a Reader in the Department of Electronics & Electrical Engineering at the University of Surrey, UK. Prior to joining Surrey he was a Post-Doctoral Research Fellow in the Department of Electronics at Southampton University, UK, from 1979 to 1983. He received his MSc and PhD degrees in Control Engineering and Measurement & Instrumentation from University of Bradford, UK, in 1975 and 1979 respectively. He received the BSc degree in Physical Sciences from University of Ceylon, Colombo, Sri Lanka, in 1971. After serving as a temporary member of academic staff for nine months in University of Ceylon, Colombo, Sri Lanka, he joined as an academic member of staff in the Physics Department of Fourabay College, University of Sierra Leone in 1971 and remained until 1974. During this period he also taught Physics and mathematics in a secondary school. He was a Visiting Professor in the Driftmier Engineering Centre at University of Georgia in 1993 and 1996. He is a Visiting Professor at the University of Mansoura and Dongguan University in China. In 2004 he was a Visiting Scholar in the School of Engineering & Applied Science at University of California, Los Angeles. Professor Balachandran is a Fellow of IEEE (USA), IEE (UK), InstPhy (UK), InstMC (UK) and Royal Society of Arts (UK). His research interest spans several different disciplines: Electrostatics & Charge Particle Dynamics, Electrohydrodynamics, Micro/Nano particles and fibre generation, Transport of DNA using DEP force, Lab-on-a-chip, Electromagnetic Field Sensing, EM Interaction with Human Body, Dynamic Measurement Systems, Global Positioning Satellite System for Navigation and Medical Electronics. He has actively pursued research in these fields for more than 20 years and published over 200 papers to date and filed 10 patent applications. He has served as an External Examiner for 23 PhD degrees in the UK, France and Australia. Professor Balachandran has presented more than 40 plenary and invited talks in his field of expertise at international conferences around the globe. He has organized and Chaired several international conferences and continue to serve as a member of several scientific and organizing committees of international conferences. He is regularly invited to Chair sessions at IEEE/IAS, ICLASS, and several other meetings in the UK, Europe, USA and Asia. On a couple of occasions, Prof. Balachandran’s research has been featured on BBC World Service and TV Broadcasts. He continues to review manuscripts for 15 archival journals and research grant applications for EPSRC (UK), EU Framework, NSF (USA), SERC (Canada) and Singapore government. Prof. Balachandran is a member of the Editorial Board of the Journal of Atomization and Sprays, and the International Journal of Particle Science and Technology. He is a paper review manager of IEEE Transactions of Industrial Application Society. He has been a Guest Editor for the Journal of Measurement & Control. He has served as a member of Brunel University London Court, Council, Senate, Finance Committee, Appeals Panel, Faculty Board and Wolfson Centre Advisory Board. He has a long experience of acting as a consultant in the fields of his research to over 30 companies worldwide.

Related Research Group(s)

Biomedical Engineering

Biomedical Engineering - Research in the growing multi-disciplinary field of advanced technology as devices, processes and modelling to advance health through improvements in therapy, diagnosis, screening, monitoring and rehabilitation.


Project last modified 17/12/2020