Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT

Lee, Edward H.; Zheng, Jimmy; Colak, Errol; Mohammadzadeh, Maryam; Houshmand, Golnaz; Bevins, Nicholas; Kitamura, Felipe; Altinmakas, Emre; Reis, Eduardo Pontes; Kim, Jae-Kwang; Klochko, Chad; Han, Michelle; Moradian, Sadegh; Mohammadzadeh, Ali; Sharifian, Hashem; Hashemi, Hassan; Firouznia, Kavous; Ghanaati, Hossien; Gity, Masoumeh; Doğan, Hakan; Salehinejad, Hojjat; Alves, Henrique; Seekins, Jayne; Abdala, Nitamar; Atasoy, Çetin; Pouraliakbar, Hamidreza; Maleki, Majid; Wong, S. Simon; Yeom, Kristen W.

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.