Abstract

Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.

Highlights

  • Since the appearance of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first announced in China and the World Health Organization declared the COVID-19 outbreak a pandemic, the number of infected and dead cases has mounted

  • Precision involves the ratio between TP and (TP + false positives (FPs)), where TP is the number of true positives and FP is the number of FPs

  • The SARS-CoV-2 pandemic has increased the requirement of PCR tests for diagnosis

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Summary

Introduction

Since the appearance of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first announced in China and the World Health Organization declared the COVID-19 outbreak a pandemic, the number of infected and dead cases has mounted. Developing new strategies for managing the pandemic are required [1] because effective medications, treatments, or vaccines have not been developed far. Studies predicting the structures of coronavirus proteins have been conducted during the pandemic [3,4,5]. Other fronts include an exchange of knowledge, the evolution of the pandemic, the support of healthcare personnel, and its application as a tool for population control [6,7,8,9]

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