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Deep Learning Models for Predicting Severe Progression in COVID-19-infected Patients
Thao Thi Ho;
Jongmin Park;
Taewoo Kim;
Byunggeon Park;
Jaehee Lee;
Jin Young Kim;
Ki Beom Kim;
Sooyoung Choi;
Young Hwan Kim;
Jae-Kwang Lim;
Sanghun Choi
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:
Ho TT, Park J, Kim T, Park B, Lee J, Kim JY, Kim KB, Choi S, Kim YH, Lim JK, Choi S
Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study