ABSTRACT
With the spread of SARS-CoV-2 variants with higher transmissibility and disease severity, rapid detection and isolation of patients remains a critical step in the control of the pandemic. RT-PCR is the recommended diagnostic test for the diagnosis of COVID-19. The current study aims to develop an artificial intelligence (AI)-driven COVID-19 RT-PCR detection system for rapid and reliable diagnosis, facilitating the heavy burden of healthcare workers. A multi-input deep convolutional neural network (DCNN) is proposed. A MobileNetV2 DCNN architecture was used to predict the possible diagnostic result of RT-PCR fluorescence data from patient nasopharyngeal sample analyses. Amplification curves in FAM (ORF1ab and N genes, SARS-CoV-2) and HEX (human RNAse P gene, internal control) channels of 400 samples were categorized as positive, weak-positive, negative or re-run (unspecific fluorescence). During the network training, HEX and FAM channel images for each sample were simultaneously presented to the DCNN. The obtained DCNN model was verified using another 160 new test samples. The proposed DCNN classified RT-PCR amplification curves correctly for all COVID-19 diagnostic categories with an accuracy, sensitivity, specificity, F1-score, and AUC of the model reported to be 1. Furthermore, the performance of other pre-trained well-known DCNN models was also compared with the MobileNetV2 model using 5-fold cross-validation, and the results showed that there were no significant differences between the other models at the 5% significance level;however, the MobileNetV2 model outperformed others dramatically in terms of the training speed and fast convergence. The developed model can help rapidly diagnose COVID-19 patients and would be beneficial in tackling future pandemics.