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Deep learning models for predicting severe progression in COVID-19-infected patients
Sanghun Choi;
Jae-Kwang Lim;
Thao Thi Ho;
Jongmin Park;
Taewoo Kim;
Byunggeon Park;
Jaehee Lee;
Jin Young Kim;
Ki Beom Kim;
Sooyoung Choi;
Young Hwan Kim
ABSTRACT
Background:
Many COVID-19 patients rapidly progress into respiratory failure with a broad range of severity. Identification of the high-risk cases is critical for early intervention.
Objective:
The aim of this study is to develop deep learning models that can rapidly diagnose 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 model (ACNN) including an artificial neural network for clinical data and a convolution-neural network for 3D CT imaging data is developed to classify high-risk cases with a severe progression (event) from low-risk COVID-19 patients (event-free).
Results:
By using the mixed ACNN model, we could obtain high classification performance using novel coronavirus pneumonia (NCP) lesion images (93.9% accuracy, 80.8% sensitivity, 96.9% specificity, and 0.916 AUC) and using lung segmentation images (94.3% accuracy, 74.7% sensitivity, 95.9% specificity, and 0.928 AUC) for event vs. event-free groups.
Conclusions:
Our study has successfully differentiated high-risk cases among COVID-19 patients using the imaging and clinical features of COVID-19 patients. The developed model is potentially utilized as a prediction tool for intervening active therapy.
Citation
Please cite as:
Choi S, Lim JK, Ho TT, Park J, Kim T, Park B, Lee J, Kim JY, Kim KB, Choi S, Kim YH
Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study