ABSTRACT
COVID-19 is spreading widely, and the pandemic is seriously threatening public health throughout the world. A comprehensive study on the optimal sampling types and timing for an efficient SARS-CoV-2 test has not been reported. We collected clinical information and the values of 55 biochemical indices for 237 COVID-19 patients, with 37 matched non-COVID-19 pneumonia patients and 131 healthy people in Inner Mongolia as control. In addition, the results of dynamic detection of SARS-CoV-2 using oropharynx swab, pharynx swab, and feces were collected from 197 COVID-19 patients. SARS-CoV-2 RNA positive in feces specimen was present in approximately one-third of COVID-19 patients. The positive detection rate of SARS-CoV-2 RNA in feces was significantly higher than both in the oropharynx and nasopharynx swab (P < 0.05) in the late period of the disease, which is not the case in the early period of the disease. There were statistically significant differences in the levels of blood LDH, CRP, platelet count, neutrophilic granulocyte count, white blood cell number, and lymphocyte count between COVID-19 and non-COVID-19 pneumonia patients. Finally, we developed and compared five machine-learning models to predict the prognosis of COVID-19 patients based on biochemical indices at disease onset and demographic characteristics. The best model achieved an area under the curve of 0.853 in the 10-fold cross-validation.
ABSTRACT
Objectives: COVID-19 is highly infectious and has been widely spread worldwide, with more than 159 million confirmed cases and more than 3 million deaths as of May 11, 2021. It has become a serious public health event threatening people's lives and safety. Due to the rapid transmission and long incubation period, shortage of medical resources would easily occur in the short term of discovering disease cases. Therefore, we aimed to construct an artificial intelligent framework to rapidly distinguish patients with COVID-19 from common pneumonia and non-pneumonia populations based on computed tomography (CT) images. Furthermore, we explored artificial intelligence (AI) algorithms to integrate CT features and laboratory findings on admission to predict the clinical classification of COVID-19. This will ease the burden of doctors in this emergency period and aid them to perform timely and appropriate treatment on patients. Methods: We collected all CT images and clinical data of novel coronavirus pneumonia cases in Inner Mongolia, including domestic cases and those imported from abroad; then, three models based on transfer learning to distinguish COVID-19 from other pneumonia and non-pneumonia population were developed. In addition, CT features and laboratory findings on admission were combined to predict clinical types of COVID-19 using AI algorithms. Lastly, Spearman's correlation test was applied to study correlations of CT characteristics and laboratory findings. Results: Among three models to distinguish COVID-19 based on CT, vgg19 showed excellent diagnostic performance, with area under the curve (AUC) of the receiver operating characteristic (ROC) curve at 95%. Together with laboratory findings, we were able to predict clinical types of COVID-19 with AUC of the ROC curve at 90%. Furthermore, biochemical markers, such as C-reactive protein (CRP), LYM, and lactic dehydrogenase (LDH) were identified and correlated with CT features. Conclusion: We developed an AI model to identify patients who were positive for COVID-19 according to the results of the first CT examination after admission and predict the progression combined with laboratory findings. In addition, we obtained important clinical characteristics that correlated with the CT image features. Together, our AI system could rapidly diagnose COVID-19 and predict clinical types to assist clinicians perform appropriate clinical management.
ABSTRACT
Objective: To analyze the clinical classification and chest CT manifestations of coronavirus disease 2019(COVID-19)in asymptomatic infection transferred to diagnosis, and improve image understanding of asymptomatic infection COVID-19.
ABSTRACT
COVID-19 has spread globally with over 90,000,000 incidences and 1,930,000 deaths by Jan 11, 2021, which poses a big threat to public health. It is urgent to distinguish COVID-19 from common pneumonia. In this study, we reported multiple clinical feature analyses on COVID-19 in Inner Mongolia for the first time. We dynamically monitored multiple clinical features of all 75 confirmed COVID-19 patients, 219 pneumonia patients, and 68 matched healthy people in Inner Mongolia. Then, we studied the association between COVID-19 and clinical characteristics, based on which to construct a novel logistic regression model for predicting COVID-19. As a result, among the tested clinical characteristics, WBC, hemoglobin, C-reactive protein (CRP), ALT, and Cr were significantly different between COVID-19 patients and patients in other groups. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was 0.869 for the logistic regression model using multiple factors associated with COVID-19. Furthermore, the CRP reaction showed five different time-series patterns with one-peak and double-peak modes. In conclusion, our study identified a few clinical characteristics significantly different between COVID-19 patients and others in Inner Mongolia. The features can be used to establish a reliable logistic regression model for predicting COVID-19.