فهرست نشریات علمی دسترسی آزاد ایران

Background: Early prediction of disease progression in coronavirus disease 2019 (COVID-19) patients can be helpful for personalized therapy, as well as the optimal allocation of public health resources. Objectives: This study aimed to present predictive models for identifying potential high-risk COVID-19 patients upon hospital admission, based on the examination of clinical and radiological features by radiologists and artificial intelligence (AI). Results: Overall, 238 out of 786 patients (30.3%) progressed into severe or critical pneumonia during the 14-day follow-up. Nine clinical findings, 17 laboratory indicators, 48 physician-extracted radiological features of pneumonia lesions, and 126 AI-driven radiological features were collected. The urea, albumin level, and lesion size in the basal segment of the right lower lobe of the lung or the proportion of CT values in the range of -200 - 60 in the left lung were the representative features for constructing the R-Doc and R-AI models, respectively. Comparison of the R-Doc model (AUC: 0.840, 95% CI: 0.747 - 0.933 for the training set and 0.731, 95% CI: 0.606 - 0.857 for the test set) with the R-AI model (AUC: 0.803, 95% CI: 0.701 - 0.906 for the training set and AUC: 0.731, 95% CI: 0.606 - 0.857 for the validation set) indicated a marginal difference in identifying patients at risk of progression to pneumonia upon admission (P < 0.1). The R-AI model was superior to model C, with an AUC of 0.770 for the training set (95% CI: 0.657 - 0.882) and 0.666 for the validation set to identify high-risk non-severe cases upon admission. Conclusion: By using radiological features along with blood tests, early identification of COVID-19 patients, who are at risk of disease progression, can be achieved on admission (rapidly by using AI); therefore, the use of these features can contribute to the clinical management of COVID-19.