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VIDIIA Hunter: a low-cost, smartphone connected, artificial intelligence-assisted COVID-19 rapid diagnostic platform approved for medical use in the UK
Introduction: Accurate and rapid diagnostics paired with effective tracking and tracing systems are key to halting the spread of infectious diseases, limiting the emergence of new variants and to monitor vaccine efficacy. The current gold standard test (RT-qPCR) for COVID-19 is highly accurate and s...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10572354/ https://www.ncbi.nlm.nih.gov/pubmed/37842636 http://dx.doi.org/10.3389/fmolb.2023.1144001 |
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author | Poirier, Aurore C. Riaño Moreno, Ruben D. Takaindisa, Leona Carpenter, Jessie Mehat, Jai W. Haddon, Abi Rohaim, Mohammed A. Williams, Craig Burkhart, Peter Conlon, Chris Wilson, Matthew McClumpha, Matthew Stedman, Anna Cordoni, Guido Branavan, Manoharanehru Tharmakulasingam, Mukunthan Chaudhry, Nouman S. Locker, Nicolas Fernando, Anil Balachandran, Wamadeva Bullen, Mark Collins, Nadine Rimer, David Horton, Daniel L. Munir, Muhammad La Ragione, Roberto M. |
author_facet | Poirier, Aurore C. Riaño Moreno, Ruben D. Takaindisa, Leona Carpenter, Jessie Mehat, Jai W. Haddon, Abi Rohaim, Mohammed A. Williams, Craig Burkhart, Peter Conlon, Chris Wilson, Matthew McClumpha, Matthew Stedman, Anna Cordoni, Guido Branavan, Manoharanehru Tharmakulasingam, Mukunthan Chaudhry, Nouman S. Locker, Nicolas Fernando, Anil Balachandran, Wamadeva Bullen, Mark Collins, Nadine Rimer, David Horton, Daniel L. Munir, Muhammad La Ragione, Roberto M. |
author_sort | Poirier, Aurore C. |
collection | PubMed |
description | Introduction: Accurate and rapid diagnostics paired with effective tracking and tracing systems are key to halting the spread of infectious diseases, limiting the emergence of new variants and to monitor vaccine efficacy. The current gold standard test (RT-qPCR) for COVID-19 is highly accurate and sensitive, but is time-consuming, and requires expensive specialised, lab-based equipment. Methods: Herein, we report on the development of a SARS-CoV-2 (COVID-19) rapid and inexpensive diagnostic platform that relies on a reverse-transcription loop-mediated isothermal amplification (RT-LAMP) assay and a portable smart diagnostic device. Automated image acquisition and an Artificial Intelligence (AI) deep learning model embedded in the Virus Hunter 6 (VH6) device allow to remove any subjectivity in the interpretation of results. The VH6 device is also linked to a smartphone companion application that registers patients for swab collection and manages the entire process, thus ensuring tests are traced and data securely stored. Results: Our designed AI-implemented diagnostic platform recognises the nucleocapsid protein gene of SARS-CoV-2 with high analytical sensitivity and specificity. A total of 752 NHS patient samples, 367 confirmed positives for coronavirus disease (COVID-19) and 385 negatives, were used for the development and validation of the test and the AI-assisted platform. The smart diagnostic platform was then used to test 150 positive clinical samples covering a dynamic range of clinically meaningful viral loads and 250 negative samples. When compared to RT-qPCR, our AI-assisted diagnostics platform was shown to be reliable, highly specific (100%) and sensitive (98–100% depending on viral load) with a limit of detection of 1.4 copies of RNA per µL in 30 min. Using this data, our CE-IVD and MHRA approved test and associated diagnostic platform has been approved for medical use in the United Kingdom under the UK Health Security Agency’s Medical Devices (Coronavirus Test Device Approvals, CTDA) Regulations 2022. Laboratory and in-silico data presented here also indicates that the VIDIIA diagnostic platform is able to detect the main variants of concern in the United Kingdom (September 2023). Discussion: This system could provide an efficient, time and cost-effective platform to diagnose SARS-CoV-2 and other infectious diseases in resource-limited settings. |
format | Online Article Text |
id | pubmed-10572354 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105723542023-10-14 VIDIIA Hunter: a low-cost, smartphone connected, artificial intelligence-assisted COVID-19 rapid diagnostic platform approved for medical use in the UK Poirier, Aurore C. Riaño Moreno, Ruben D. Takaindisa, Leona Carpenter, Jessie Mehat, Jai W. Haddon, Abi Rohaim, Mohammed A. Williams, Craig Burkhart, Peter Conlon, Chris Wilson, Matthew McClumpha, Matthew Stedman, Anna Cordoni, Guido Branavan, Manoharanehru Tharmakulasingam, Mukunthan Chaudhry, Nouman S. Locker, Nicolas Fernando, Anil Balachandran, Wamadeva Bullen, Mark Collins, Nadine Rimer, David Horton, Daniel L. Munir, Muhammad La Ragione, Roberto M. Front Mol Biosci Molecular Biosciences Introduction: Accurate and rapid diagnostics paired with effective tracking and tracing systems are key to halting the spread of infectious diseases, limiting the emergence of new variants and to monitor vaccine efficacy. The current gold standard test (RT-qPCR) for COVID-19 is highly accurate and sensitive, but is time-consuming, and requires expensive specialised, lab-based equipment. Methods: Herein, we report on the development of a SARS-CoV-2 (COVID-19) rapid and inexpensive diagnostic platform that relies on a reverse-transcription loop-mediated isothermal amplification (RT-LAMP) assay and a portable smart diagnostic device. Automated image acquisition and an Artificial Intelligence (AI) deep learning model embedded in the Virus Hunter 6 (VH6) device allow to remove any subjectivity in the interpretation of results. The VH6 device is also linked to a smartphone companion application that registers patients for swab collection and manages the entire process, thus ensuring tests are traced and data securely stored. Results: Our designed AI-implemented diagnostic platform recognises the nucleocapsid protein gene of SARS-CoV-2 with high analytical sensitivity and specificity. A total of 752 NHS patient samples, 367 confirmed positives for coronavirus disease (COVID-19) and 385 negatives, were used for the development and validation of the test and the AI-assisted platform. The smart diagnostic platform was then used to test 150 positive clinical samples covering a dynamic range of clinically meaningful viral loads and 250 negative samples. When compared to RT-qPCR, our AI-assisted diagnostics platform was shown to be reliable, highly specific (100%) and sensitive (98–100% depending on viral load) with a limit of detection of 1.4 copies of RNA per µL in 30 min. Using this data, our CE-IVD and MHRA approved test and associated diagnostic platform has been approved for medical use in the United Kingdom under the UK Health Security Agency’s Medical Devices (Coronavirus Test Device Approvals, CTDA) Regulations 2022. Laboratory and in-silico data presented here also indicates that the VIDIIA diagnostic platform is able to detect the main variants of concern in the United Kingdom (September 2023). Discussion: This system could provide an efficient, time and cost-effective platform to diagnose SARS-CoV-2 and other infectious diseases in resource-limited settings. Frontiers Media S.A. 2023-09-28 /pmc/articles/PMC10572354/ /pubmed/37842636 http://dx.doi.org/10.3389/fmolb.2023.1144001 Text en Copyright © 2023 Poirier, Riaño Moreno, Takaindisa, Carpenter, Mehat, Haddon, Rohaim, Williams, Burkhart, Conlon, Wilson, McClumpha, Stedman, Cordoni, Branavan, Tharmakulasingam, Chaudhry, Locker, Fernando, Balachandran, Bullen, Collins, Rimer, Horton, Munir and La Ragione. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Poirier, Aurore C. Riaño Moreno, Ruben D. Takaindisa, Leona Carpenter, Jessie Mehat, Jai W. Haddon, Abi Rohaim, Mohammed A. Williams, Craig Burkhart, Peter Conlon, Chris Wilson, Matthew McClumpha, Matthew Stedman, Anna Cordoni, Guido Branavan, Manoharanehru Tharmakulasingam, Mukunthan Chaudhry, Nouman S. Locker, Nicolas Fernando, Anil Balachandran, Wamadeva Bullen, Mark Collins, Nadine Rimer, David Horton, Daniel L. Munir, Muhammad La Ragione, Roberto M. VIDIIA Hunter: a low-cost, smartphone connected, artificial intelligence-assisted COVID-19 rapid diagnostic platform approved for medical use in the UK |
title | VIDIIA Hunter: a low-cost, smartphone connected, artificial intelligence-assisted COVID-19 rapid diagnostic platform approved for medical use in the UK |
title_full | VIDIIA Hunter: a low-cost, smartphone connected, artificial intelligence-assisted COVID-19 rapid diagnostic platform approved for medical use in the UK |
title_fullStr | VIDIIA Hunter: a low-cost, smartphone connected, artificial intelligence-assisted COVID-19 rapid diagnostic platform approved for medical use in the UK |
title_full_unstemmed | VIDIIA Hunter: a low-cost, smartphone connected, artificial intelligence-assisted COVID-19 rapid diagnostic platform approved for medical use in the UK |
title_short | VIDIIA Hunter: a low-cost, smartphone connected, artificial intelligence-assisted COVID-19 rapid diagnostic platform approved for medical use in the UK |
title_sort | vidiia hunter: a low-cost, smartphone connected, artificial intelligence-assisted covid-19 rapid diagnostic platform approved for medical use in the uk |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10572354/ https://www.ncbi.nlm.nih.gov/pubmed/37842636 http://dx.doi.org/10.3389/fmolb.2023.1144001 |
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