Specificity of SARS-CoV-2 Real-Time PCR Improved by Deep Learning Analysis

Abstract

Real-time PCR (RT-PCR) is widely used to diagnose human pathogens. RT-PCR data are traditionally analyzed by estimating the threshold cycle (C_T ) at which the fluorescence signal produced by emission of a probe crosses a baseline level. Current models used to estimate the C_T value are based on approximations that do not adequately account for the stochastic variations of the fluorescence signal that is detected during RT-PCR. Less common deviations become more apparent as the sample size increases, as is the case in the current SARS-CoV-2 pandemic. In this work, we employ a method independent of C_T value to interpret RT-PCR data. In this novel approach, we built and trained a deep learning model, qPCRdeepNet, to analyze the fluorescent readings obtained during RT-PCR. We describe how this model can be deployed as a quality assurance tool to monitor result interpretation in real time. The model's performance with the TaqPath COVID19 Combo Kit assay, widely used for SARS-CoV-2 detection, is described. This model can be applied broadly for the primary interpretation of RT-PCR assays and potentially replace the C_T interpretive paradigm.

Keywords: COVID-19; RT-PCR; SARS-CoV-2; TaqPath; artificial intelligence; deep learning; real-time PCR.