Deep Learning Models for Predicting Severe Progression in COVID-19-infected Patients.

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Abstract

Many COVID-19 patients rapidly progress to respiratory failure with a broad range of severity. Identification of high-risk cases is critical for an early intervention.
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.
We analyzed 297 COVID-19 patients from five hospitals in Daegu, South Korea. A mixed artificial convolution neural network (ACNN) model, combining an artificial neural network for clinical data and a convolution neural network for 3D CT imaging data, is developed to classify these cases as either high risk of severe progression (event) or low risk (event-free).
Using the mixed ACNN model, we were able to 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 lung segmentation images (94.3% accuracy, 74.7% sensitivity, 95.9% specificity, and 0.928 AUC) for event vs. event-free groups.
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.