Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT - Edward H. Lee; Jimmy Zheng; Errol Colak; Maryam Mohammadzadeh; Golnaz Houshmand; Nicholas Bevins; Felipe Kitamura; Emre Altinmakas; Eduardo Pontes Reis; Jae-Kwang Kim; Chad Klochko; Michelle Han; et al. (npj Digital Medicine, 2021)
Abstract 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.