## 1 Introduction  
The ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, local outbreaks of Middle East respiratory syndrome (MERS) and Ebola virus disease (EVD), as well as emerging pan-drug resistant bacterial pathogens represent a major public health concern. Rapid and accurate detection and identification of pathogens is therefore of major significance for the timely management of patients and prevention of the spread of outbreaks. Molecular assays are routinely used for the detection of human pathogens in clinical microbiology laboratories due to their high sensitivity and specificity. To date, coronavirus disease 2019 (COVID-19) has been responsible for over 6.4 million deaths worldwide [1]. With the acceleration of the COVID-19 pandemic due to emerging SARS-CoV-2 immune escape variants with higher transmissibility and disease severity, timely detection and isolation of COVID-19 patients remain a critical step in the control of the pandemic. Real-time reverse-transcription polymerase chain reaction (RT-PCR) tests have been extensively exploited worldwide since the beginning of the pandemic. These tests detect the SARS-CoV-2 viral RNA signatures to confirm the presence of SARS-CoV-2 in a patient nasopharyngeal sample. RT-PCR combines PCR amplification chemistry with the detection of amplified products via fluorescent probes for determining the presence of pathogen-specific genetic material [2]. The analysis of RT-PCR data is traditionally performed by estimating the threshold cycle (Ct) at which the exponential phase of the fluorescence signal crosses a baseline threshold. Determination of a positive or negative RT-PCR result is based on appropriate setting of threshold. Stochastic variations such as noise in the fluorescence signal may affect the sigmoidal function in RT-PCR assays. Besides threshold determination, amplification artifacts during the amplification process may lead to false positivity in a given sample. Non-specific amplification may result from primer dimerization or due to cross-reaction with non-target genetic material leading to non-specific fluorescence signals in the patient sample [3]. Therefore, analysis of RT-PCR fluorescence data by experienced personnel is fundamental for the accurate diagnosis of COVID-19 patients. Rapid tracking, identification and isolation of SARS-CoV-2-infected individuals remains a critical step for the implementation of early interventions and curbing the spread of the pandemic. Considering the large volume of patient samples and burden of intensive laboratory testing, the use of deep learning for automated identification and classification of RT-PCR assay results represents a time- and labor-efficient technology. Artificial intelligence (AI) algorithms have been widely used in diagnosis, clinical decision making, patient management, social control, surveillance, therapeutics and vaccine development [4]. In terms of diagnosis, the most explored area of AI for COVID-19 was AI-assisted diagnosis through chest CT imaging using a convolutional neural network (CNN). A number of studies have proposed the use of deep learning for accurate and sensitive diagnosis and have shown AI models to achieve a higher diagnostic performance compared to radiologists [5; 6; 7; 8]. The use of different machine learning models to classify patients into mild and severe cases of COVID-19 based on multivariate blood testing has also been previously shown [10]. Alternatively, AI has been considered as an emerging diagnostic tool in clinical microbiology laboratories. Application of AI-based testing has been proposed to improve the turnaround time, quality and cost of microbiological tests and has been applied to automated urine analysis [11] and the detection of bacteria [12; 13; 14; 15], protozoa and parasites [16; 17; 18; 19; 20] in clinical samples via image analysis, as well as to antimicrobial susceptibility testing [21; 22; 23; 24].