Abstract
BackgroundThis study intends to establish a combined prediction model that integrates the clinical symptoms,the lung lesion volume, and the radiomics features of patients with COVID-19, resulting in a new model to predict the severity of COVID-19.MethodsThe clinical data of 386 patients with COVID-19 at several hospitals, as well as images of certain patients during their hospitalization, were collected retrospectively to create a database of patients with COVID-19 pneumonia. The contour of lungs and lesion locations may be retrieved from CT scans using a CT-image-based quantitative discrimination and trend analysis method for COVID-19 and the Mask R-CNN deep neural network model to create 3D data of lung lesions. The quantitative COVID-19 factors were then determined, on which the diagnosis of the development of the patients' symptoms could be established. Then, using an artificial neural network, a prediction model of the severity of COVID-19 was constructed by combining characteristic imaging features on CT slices with clinical factors. ANN neural network was used for training, and tenfold cross-validation was used to verify the prediction model. The diagnostic performance of this model is verified by the receiver operating characteristic (ROC) curve.ResultsCT radiomics features extraction and analysis based on a deep neural network can detect COVID-19 patients with an 86% sensitivity and an 85% specificity. According to the ROC curve, the constructed severity prediction model indicates that the AUC of patients with severe COVID-19 is 0.761, with sensitivity and specificity of 79.1% and 73.1%, respectively.ConclusionsThe combined prediction model for severe COVID-19 pneumonia, which is based on deep learning and integrates clinical aspects, pulmonary lesion volume, and radiomics features of patients, has a remarkable differential ability for predicting the course of disease in COVID-19 patients. This may assist in the early prevention of severe COVID-19 symptoms.
Highlights
This study intends to establish a combined prediction model that integrates the clinical symptoms,the lung lesion volume, and the radiomics features of patients with COVID-19, resulting in a new model to predict the severity of COVID-19
Construction of a database of patients with COVID‐19 Of 386 confirmed patients with COVID-19 included in this research, 205 had Computed tomography (CT) image specimens (Table 1); 207 (53.6%) were men and 179(46.4%) were women; the mean age of the patients was 57.3 years old; 362 (93.8%) had no previous smoking history; 293 (75.9%) had a history of underlining diseases, of which 45.3% had a history of two or more underlining diseases among which hypertension (123 cases) and diabetes (41 cases) being the most common; in the classification of severity of illness at the time of hospital admission, 45.6% of patients were mildly ill and 54.4% were critically or severely ill
The data presented above suggested that the objects included in this research research can fully reflect the overall characteristics of the current COVID-19 patient population
Summary
This study intends to establish a combined prediction model that integrates the clinical symptoms,the lung lesion volume, and the radiomics features of patients with COVID-19, resulting in a new model to predict the severity of COVID-19. Pneumonia is a highly contagious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) infection that emerged in December 2019 [1, 2]. Chen et al BMC Medical Imaging (2022) 22:29 represents a wide spectrum of clinical manifestations, including fever, cough, and fatigue, which may cause fatal acute respiratory distress syndromes [4]. COVID-19 has been proven to be infectious from person to person [5], and the World Health Organization (WHO) has declared COVID-19 a pandemic [6]. Planning for early intervention and enhancing surveillance is critical in the event of a pandemic
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
