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.
【저자키워드】 viral infection, Computational science, Radiography, 【초록키워드】 COVID-19, Coronavirus disease 2019, Prognosis, deep learning, Pneumonia, COVID, CNN, Chest computed tomography, Chest CT, Accuracy, International, Patient, COVID-19 diagnosis, Follow-up, utility, predict, diagnose, Volume, Non-COVID-19, disease course, Inclusion, controls, deep, shown, significantly, clinically, question, automatically, DCD, 【제목키워드】 COVID-19, Prognosis, lung, COVID, Chest CT, International, deep,