ABSTRACT
COVID-19 associated lung diseases can mimic radiological characteristics of other viral lung diseases such as influenza which may lead to misdiagnosis. In this study, we proposed an Artificial Intelligence framework based on a combination of a Convolutional Neural network architecture and a Recurrent Neural Network architecture to classify CT volumes with COVID-19, Influenza, and no-infection. The model was trained on a dataset of 300 patients (100 patients in each class). Each set of 15 consecutive axial slices with the associated label of the corresponding CT volume was input as a 3 channel input at 5 time points to the CNN-RNN network. Benchmarked against RT-PCR confirmed cases of COVID-19 and Influenza, our model, when evaluated on an independent validation set of 400 CT patients, can accurately classify CT volumes of patients with COVID-19, Influenza, or no-infection with a sensitivity of 96% (COVID-19) and 95% (Influenza) (Tablel). Figurel shows the percentage of correctly classified and misclassified cases in each class. Our model provides rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.