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Detect Severe COVID-19 Infection: A Signature of Chest CT and Laboratory Measurement
Fang Zhu;
Daowei Li;
Qiang Zhang;
Yue Tan;
Yuanyi Yue;
Yuhan Bai;
Jimeng Li;
Jiahang Li;
Xinghuo Feng;
Shiyu Chen;
Youjun Xu;
Si-Yu Xiao;
Muyan Sun;
Xiaona Li
ABSTRACT
Background:
Most of mortality of COVID-19 were from severe patients. Effective treatment of these severe cases remains a challenge due to a lack of early detection.
Objective:
Herein, we presented a prediction model, combining the radiological outcome with the clinical biochemical indexes, to identify the severe cases.
Methods:
A total set of 27 severe and 151 non-severe clinical and CT (computerized tomography) records from 46 COVID-19 patients (10 severe, 36 non-severe) was collected for building the model. Using a recent published CNN (convolutional neural network), we managed to extract features from CT images. A machine learning model which combines these features with clinical laboratory results also was trained.
Results:
Herein, we presented a prediction model, combining the radiological outcome with the clinical biochemical indexes, to identify the severe cases. The prediction model yields a cross-validated AUROC score of 0.93 and F1 score of 0.89, which improved 6% and 15%, respectively, from the models with laboratory tests features only. In addition, we also developed a statistical model for forecasting severity based on patients’ laboratory tests results before turning severe cases, with an AUROC score of 0.81.
Conclusions:
To our knowledge, this is the first report to predict COVID-19 patient’s severity progression, as well as severity forecasting, through a combination analysis of laboratory tests and CT images.
Citation
Please cite as:
Zhu F, Li D, Zhang Q, Tan Y, Yue Y, Bai Y, Li J, Li J, Feng X, Chen S, Xu Y, Xiao SY, Sun M, Li X
Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach