| Literature DB >> 33514852 |
Edward H Lee1, Jimmy Zheng2, Errol Colak3, Maryam Mohammadzadeh4, Golnaz Houshmand5, Nicholas Bevins6, Felipe Kitamura7, Emre Altinmakas8, Eduardo Pontes Reis9, Jae-Kwang Kim10, Chad Klochko5, Michelle Han2, Sadegh Moradian11, Ali Mohammadzadeh5, Hashem Sharifian4, Hassan Hashemi12, Kavous Firouznia12, Hossien Ghanaati12, Masoumeh Gity12, Hakan Doğan8, Hojjat Salehinejad3, Henrique Alves7, Jayne Seekins2, Nitamar Abdala7, Çetin Atasoy8, Hamidreza Pouraliakbar5, Majid Maleki5, S Simon Wong13, Kristen W Yeom14.
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.Entities:
Year: 2021 PMID： 33514852 DOI： 10.1038/s41746-020-00369-1
Source DB: PubMed Journal: NPJ Digit Med ISSN： 2398-6352