Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2

  1. Mohammed A. Rohaim
  2. Emily Clayton
  3. Irem Sahin
  4. Julianne Vilela
  5. Manar E. Khalifa
  6. Mohammad Q. Al-Natour
  7. Mahmoud Bayoumi
  8. Aurore C. Poirier
  9. Manoharanehru Branavan
  10. Mukunthan Tharmakulasingam
  11. Nouman S. Chaudhry
  12. Ravinder Sodi
  13. Amy Brown
  14. Peter Burkhart
  15. Wendy Hacking
  16. Judy Botham
  17. Joe Boyce
  18. Hayley Wilkinson
  19. Craig Williams
  20. Jayde Whittingham-Dowd
  21. Elisabeth Shaw
  22. Matt Hodges
  23. Lisa Butler
  24. Michelle D. Bates
  25. Roberto La Ragione
  26. Wamadeva Balachandran
  27. Anil Fernando
  28. Muhammad Munir

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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.

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  1. Version published to 10.3390/v12090972
  2. Version published to 10.1101/2020.07.08.20148999v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.07.08.20148999: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The following are available online at www.mdpi.com/xxx/s1, Figure S1: Optimization and raw data on the LAMP optimization, Table S1: Data on the comparison of the qRT-PCR and LAMP.
    LAMP
    suggested: (LAMP, RRID:SCR_001740)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  4. Version published to 10.1101/2020.07.08.20148999v1 on medRxiv