%0 Journal Article
%@ 2291-9694
%I JMIR Publications
%V 9
%N 1
%P e24973
%T Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study
%A Ho,Thao Thi
%A Park,Jongmin
%A Kim,Taewoo
%A Park,Byunggeon
%A Lee,Jaehee
%A Kim,Jin Young
%A Kim,Ki Beom
%A Choi,Sooyoung
%A Kim,Young Hwan
%A Lim,Jae-Kwang
%A Choi,Sanghun
%+ School of Mechanical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 41566, Republic of Korea, 82 53 950 5578, s-choi@knu.ac.kr
%K COVID-19
%K deep learning
%K artificial neural network
%K convolutional neural network
%K lung CT
%D 2021
%7 28.1.2021
%9 Original Paper
%J JMIR Med Inform
%G English
%X Background: Many COVID-19 patients rapidly progress to respiratory failure with a broad range of severities. Identification of high-risk cases is critical for early intervention. Objective: The aim of this study is to develop deep learning models that can rapidly identify high-risk COVID-19 patients based on computed tomography (CT) images and clinical data. Methods: We analyzed 297 COVID-19 patients from five hospitals in Daegu, South Korea. A mixed artificial convolutional neural network (ACNN) model, combining an artificial neural network for clinical data and a convolutional neural network for 3D CT imaging data, was developed to classify these cases as either high risk of severe progression (ie, event) or low risk (ie, event-free). Results: Using the mixed ACNN model, we were able to obtain high classification performance using novel coronavirus pneumonia lesion images (ie, 93.9% accuracy, 80.8% sensitivity, 96.9% specificity, and 0.916 area under the curve [AUC] score) and lung segmentation images (ie, 94.3% accuracy, 74.7% sensitivity, 95.9% specificity, and 0.928 AUC score) for event versus event-free groups. Conclusions: Our study successfully differentiated high-risk cases among COVID-19 patients using imaging and clinical features. The developed model can be used as a predictive tool for interventions in aggressive therapies. 
%M 33455900
%R 10.2196/24973
%U http://medinform.jmir.org/2021/1/e24973/
%U https://doi.org/10.2196/24973
%U http://www.ncbi.nlm.nih.gov/pubmed/33455900