Publication: Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT
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KU Authors
Co-Authors
Lee, Edward H.
Zheng, Jimmy
Colak, Errol
Mohammadzadeh, Maryam
Houshmand, Golnaz
Bevins, Nicholas
Kitamura, Felipe
Reis, Eduardo Pontes
Kim, Jae-Kwang
Klochko, Chad
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English
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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.
Description
Source:
NPJ Digital Medicine
Publisher:
Nature Publishing Group (NPG)
Keywords:
Subject
Medicine, Health care sciences and services, Medical informatics