Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/23853
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dc.contributor.authorTharmakulasingam, M-
dc.contributor.authorChaudhry, NS-
dc.contributor.authorFernando, A-
dc.contributor.authorBranavan, M-
dc.contributor.authorBalachandran, W-
dc.contributor.authorPoirier, AC-
dc.contributor.authorRohaim, MA-
dc.contributor.authorMunir, M-
dc.contributor.authorLa Ragione, RM-
dc.date.accessioned2021-12-31T09:33:28Z-
dc.date.available2021-12-31T09:33:28Z-
dc.date.issued2021-08-26-
dc.identifier2065-
dc.identifier.citationTharmakulasingam, M., Chaudhry, N. S., Branavan, M., Balachandran, W., Poirier, A.C., Rohaim, M.A., Munir, M., La Ragione, R.M. and Fernando, A. (2021) ‘An Artificial Intelligence-Assisted Portable Low-Cost Device for the Rapid Detection of SARS-CoV-2’, Electronics, 10 (17), 2065, pp. 1-13. doi: 10.3390/electronics10172065.en_US
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/23853-
dc.description.abstractCopyright: © 2021 by the authors. 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.en_US
dc.description.sponsorshipUK Biotechnology and Bioscience Research Council (BBSRC) grant (Reference BB/R012695/1); Zoetis via the vHive initiative at the University of Surrey; EPSRC Impact Acceleration Account Seed Funding at Lancaster University.en_US
dc.format.extent1 - 13-
dc.format.mediumElectronic-
dc.language.isoen_USen_US
dc.publisherMDPI AGen_US
dc.rightsCopyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectartificial intelligenceen_US
dc.subjectSARS-CoV-2en_US
dc.subjectrapid detectionen_US
dc.subjectportable deviceen_US
dc.subjectimage processingen_US
dc.subjectLAMPen_US
dc.titleAn Artificial Intelligence-Assisted Portable Low-Cost Device for the Rapid Detection of SARS-CoV-2en_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.3390/electronics10172065-
dc.relation.isPartOfElectronics (Switzerland)-
pubs.issue17-
pubs.publication-statusPublished-
pubs.volume10-
dc.identifier.eissn2079-9292-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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