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. 2020 Sep 1;12(9):972.
doi: 10.3390/v12090972.

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

Mohammed A Rohaim  1 , Emily Clayton  1 , Irem Sahin  1 , Julianne Vilela  1 , Manar E Khalifa  1 , Mohammad Q Al-Natour  1 , Mahmoud Bayoumi  1 , Aurore C Poirier  2 , Manoharanehru Branavan  3 , Mukunthan Tharmakulasingam  4 , Nouman S Chaudhry  4 , Ravinder Sodi  5 , Amy Brown  6 , Peter Burkhart  6 , Wendy Hacking  6 , Judy Botham  6 , Joe Boyce  6 , Hayley Wilkinson  6 , Craig Williams  6 , Jayde Whittingham-Dowd  1 , Elisabeth Shaw  1 , Matt Hodges  1 , Lisa Butler  1 , Michelle D Bates  1 , Roberto La Ragione  2 , Wamadeva Balachandran  3 , Anil Fernando  4 , Muhammad Munir  1
Affiliations

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

Mohammed A Rohaim et al. Viruses. .

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.

Keywords: LAMP; SARS-CoV-2; artificial intelligence; diagnosis; point of care.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
In silico analysis of SARS-CoV-2 and primers design. (A) Genome organization of SARS-CoV-2. Scale represents an approximate position of the genome, whereas ORF1a and b are expanded to show internal gene organization. (B) Level of gene identity across the genome of the SARS-CoV-2. Identity less than 90% is not shown. (C) Primers location in the RdRP gene of SARS-COV-2 is shown. Red coloured sequences represent LAMP primers whereas blue coloured sequences are primers and probes used in the qRT-PCR. (D) Comparative sequence identity using the primers against different human coronaviruses compared to the reference SARS-CoV-2 sequence; dots represent identical nucleotides.
Figure 2
Figure 2
Sensitivities of the loop-mediated isothermal amplification (LAMP) assay. (A) Seven different dilutions of in vitro transcribed RNA were run for quantitative measurement using qRT-PCR. Relative fluorescence units show a gradient decrease in signals. (B) The corresponding PCR products on the electrophoresis gel. (C) The qRT-PCR standard curve based on the Ct value and dilution factor. (D) The serially diluted synthetic RNAs were run in the LAMP assay and colour change represents positive (yellow) or negative (pink). The lower panel shows the LAMP gradient products.
Figure 3
Figure 3
Specificity of the LAMP assay. (A) RNA extracted from different medically or respiratory important viruses as well as two dilutions of synthetic RNA were run for qPCR. (B) Corresponding PCR products were run on gel to demonstrate specificity. (C) Similar to qRT-PCR, extracted RNA were run in the LAMP assays. The top panel indicates the colorimetric detection of LAMP positive/negative reactions and the lower panel shows the electrophoresis of the corresponding LAMP products.
Figure 4
Figure 4
Fabrication and processing of LAMP data for enhanced detection of SARS-CoV-2. (A) Exterior of a smart diagnostic device (B) Description of the artificial intelligence (AI)-assisted algorithm and image processing. (C) Pipeline to process images and extraction of colorimetric information. (D) Schematic outlining the training of the network for image processing. (E) Data division for training, validation and testing of the AI model.
Figure 5
Figure 5
Conventional and AI-assisted interpretation of LAMP results. (A) Temporal analysis of known positive and negative patient samples from 360 images taken from RPi for visual interpretation of LAMP results. (B) Interpretation of corresponding patient samples by the AI-assisted LAMP results.
Figure 6
Figure 6
Clinical validation of ai-LAMP. (A–C) Comparative sample positivity between LAMP and RdRP qRT-PCR (A), LAMP and N qRT-PCR (B), LAMP and CUM qRT-PCR results (C). (D) The heatmap indicates the relative positive and negative samples among three assays. (E) Linearity chart comparing the LAMP positive/negative samples and their detection based on the RdRP gene-based qRT-PCR. (F) Linearity chart comparing the LAMP positive/negative samples and their detection based on the N gene-based qRT-PCR. (G) Naked eye detection of the first 96 samples out of the total 199 patients’ samples were processed. (H) Recovery Ct values of the miRNA spiked before RNA extraction.
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