The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course.
Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID
DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict
COVID-19 (COVID+) from non-COVID-19 (COVID−) pneumonia and normal controls. We discuss training strategies and differences
in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly
improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore,
using available follow-up scans, we investigate methods to track patient disease course and predict prognosis