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In Journal of clinical microbiology ; h5-index 74.0

Real time polymerase chain reaction (RT-PCR) is widely used to diagnose human pathogens. RT-PCR data is traditionally analyzed by estimating the threshold cycle (CT) at which the fluorescence signal produced by emission of a probe crosses a baseline level. Current models used to estimate the CT value are based on approximations that do not adequately account for the stochastic variation 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 CT value to interpret to 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 results interpretation in real-time. The model's performance with the TaqPath COVID19 Combo Kit, 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 CT interpretive paradigm.

Alouani David J, Rajapaksha Roshani R P, Jani Mehul, Rhoads Daniel D, Sadri Navid