Predicting COVID-19 Malignant Progression with AI Techniques

Background: The coronavirus disease 2019 (COVID-19) has become a worldwide pandemic since mid-December 2019, which greatly challenge public medical systems. With limited medical resources, it is a natural strategy, while adopted, to access the severity of patients then determine the treatment priority. However, our work observes the fact that the condition of many mild outpatients quickly worsens in a short time, i.e. deteriorate into severe/critical cases. Hence, it has been crucial to early identify those cases and give timely treatment for optimizing treatment strategy and reducing mortality. This study aims to establish an AI model to predict mild patients with potential malignant progression. Methods: A total of 133 consecutively mild COVID-19 patients at admission who was hospitalized in Wuhan Pulmonary Hospital from January 3 to February 13, 2020, were selected in this retrospective IRB-approved study. All mild patients at admission were categorized into groups with or without malignant progression. The clinical and laboratory data at admission, the first CT, and the follow-up CT at severe/critical stage of the two groups were compared with Chi-square test, Fisher’s exact test, and t test. Both traditional logistic regression and deep learning-based methods were used to build the prediction models. The area under ROC curve (AUC) was used to evaluate the models. Findings: The deep learning-based method significantly outperformed logistic regression (AUC 0·954 vs. 0·893). The deep learning-based method achieved a prediction AUC of 0·938 by combining the clinical data and the CT data, significantly outperforming its counterpart trained with clinical data only by 0.141. By further considering the temporal information of the CT sequence, our model achieved the best AUC of 0·954. Interpretation: The proposed model can be effectively used for finding out the mild patients who are easy to deteriorate into severe/critical cases so that such patients get timely treatments while alleviating the limitations of medical resources. Funding Statement:This work was supported by National Key R&D Program of China (No. 2018YFB1004600), HUST COVID-19 Rapid Response Call (No. 2020kfyXGYJ093, No. 2020kfyXGYJ094), National Key R&D Program of China (No.2017YFC1309100), National Science Fund for Distinguished Young Scholars (No.81925023), National Natural Science Foundation of China (No.61703049, No. 81771912, No. 81401390) Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: The authors stated this was an IRB-approved study.