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Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study

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Author(s)
Thao Thi HoJongmin ParkTaewoo KimByunggeon ParkJaehee LeeJin Young KimKi Beom KimSooyoung ChoiYoung Hwan KimJae-Kwang LimSanghun Choi
Keimyung Author(s)
Kim, Jin Young
Department
Dept. of Radiology (영상의학)
Journal Title
JMIR Med Inform
Issued Date
2021
Volume
9
Issue
1
Keyword
COVID-19deep learningartificial neural networkconvolutional neural networklung CT
Abstract
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.
Keimyung Author(s)(Kor)
김진영
Publisher
School of Medicine (의과대학)
Citation
Thao Thi Ho et al. (2021). Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study. JMIR Med Inform, 9(1), e24973. doi: 10.2196/24973
Type
Article
ISSN
2291-9694
Source
https://medinform.jmir.org/2021/1/e24973/
DOI
10.2196/24973
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/43630
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Radiology (영상의학)
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