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Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2.
Viruses. 2020 09 01; 12(9)V

Abstract

Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.

Authors+Show Affiliations

Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK.Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK.Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK.Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK.Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK.Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK.Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK.Department of Pathology and Infectious Diseases, School of Veterinary Medicine, University of Surrey, Guildford GU2 7AL, UK.College of Engineering, Design and Physical Sciences, Brunel University London, Kingston Lane, Uxbridge UB8 3PH, UK.Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK.Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK.Department of Biochemistry, Poole & Bournemouth Hospitals NHS Trust, Longfleet Road, Poole BH15 2JB, UK.The Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS, Foundation Trust, Kendal LA9 7RG, UK.The Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS, Foundation Trust, Kendal LA9 7RG, UK.The Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS, Foundation Trust, Kendal LA9 7RG, UK.The Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS, Foundation Trust, Kendal LA9 7RG, UK.The Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS, Foundation Trust, Kendal LA9 7RG, UK.The Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS, Foundation Trust, Kendal LA9 7RG, UK.The Royal Lancaster Infirmary, University Hospitals of Morecambe Bay NHS, Foundation Trust, Kendal LA9 7RG, UK.Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK.Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK.Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK.Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK.Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK.Department of Pathology and Infectious Diseases, School of Veterinary Medicine, University of Surrey, Guildford GU2 7AL, UK.College of Engineering, Design and Physical Sciences, Brunel University London, Kingston Lane, Uxbridge UB8 3PH, UK.Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK.Division of Biomedical and Life Sciences, Faculty of Health and Medicine, The Lancaster University, Lancaster LA1 4YW, UK.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

32883050

Citation

Rohaim, Mohammed A., et al. "Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2." Viruses, vol. 12, no. 9, 2020.
Rohaim MA, Clayton E, Sahin I, et al. Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2. Viruses. 2020;12(9).
Rohaim, M. A., Clayton, E., Sahin, I., Vilela, J., Khalifa, M. E., Al-Natour, M. Q., Bayoumi, M., Poirier, A. C., Branavan, M., Tharmakulasingam, M., Chaudhry, N. S., Sodi, R., Brown, A., Burkhart, P., Hacking, W., Botham, J., Boyce, J., Wilkinson, H., Williams, C., ... Munir, M. (2020). Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2. Viruses, 12(9). https://doi.org/10.3390/v12090972
Rohaim MA, et al. Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2. Viruses. 2020 09 1;12(9) PubMed PMID: 32883050.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2. AU - Rohaim,Mohammed A, AU - Clayton,Emily, AU - Sahin,Irem, AU - Vilela,Julianne, AU - Khalifa,Manar E, AU - Al-Natour,Mohammad Q, AU - Bayoumi,Mahmoud, AU - Poirier,Aurore C, AU - Branavan,Manoharanehru, AU - Tharmakulasingam,Mukunthan, AU - Chaudhry,Nouman S, AU - Sodi,Ravinder, AU - Brown,Amy, AU - Burkhart,Peter, AU - Hacking,Wendy, AU - Botham,Judy, AU - Boyce,Joe, AU - Wilkinson,Hayley, AU - Williams,Craig, AU - Whittingham-Dowd,Jayde, AU - Shaw,Elisabeth, AU - Hodges,Matt, AU - Butler,Lisa, AU - Bates,Michelle D, AU - La Ragione,Roberto, AU - Balachandran,Wamadeva, AU - Fernando,Anil, AU - Munir,Muhammad, Y1 - 2020/09/01/ PY - 2020/07/16/received PY - 2020/08/22/revised PY - 2020/08/24/accepted PY - 2020/9/5/entrez PY - 2020/9/5/pubmed PY - 2020/9/17/medline KW - LAMP KW - SARS-CoV-2 KW - artificial intelligence KW - diagnosis KW - point of care JF - Viruses JO - Viruses VL - 12 IS - 9 N2 - Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories. SN - 1999-4915 UR - https://www.unboundmedicine.com/medline/citation/32883050/Artificial_Intelligence_Assisted_Loop_Mediated_Isothermal_Amplification__AI_LAMP__for_Rapid_Detection_of_SARS_CoV_2_ L2 - https://www.mdpi.com/resolver?pii=v12090972 DB - PRIME DP - Unbound Medicine ER -