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Purpose  In this study, we propose an Artificial Intelligence framework based on 3D Convolutional Neural network (CNN) to classify CT scans of patients with COVID-19, Influenza/CAP, and no-infection, after automatic segmentation of the lungs and lung abnormalities.    Methods  The AI classification model is based on inflated 3D Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (No infection: 188, COVID-19: 230, Influenza/CAP: 249) and 210 adult patients (No infection: 70, COVID-19: 70, Influenza/CAP: 70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (No infection: 55, COVID-19: 94, Influenza/CAP: 124) and an external validation set coming from a different center (305 adult patients, COVID-19: 169, No infection: 76, Influenza/CAP: 60).    Results  The model showed excellent performance in the external validation set with an AUC of 0.90, 0.92 and 0.92 for COVID-19, Influenza/CAP and No infection respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The TRIPOD score of the proposed model is 47% (15 out of 32 TRIPOD items).    Conclusion  This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza.

An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography