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.
Highlights
In December 2019, a cluster of patients with unidentified pneumonia disease was discovered
As the methods used in model construction were relatively complex and multiple threads were covered in this study, we drew a flowchart to illustrate the experimental process (Figure 1)
We used three kinds of methods to construct models to distinguish patients with COVID-19, other common pneumonia, and normal controls based on the chest computed tomography (CT) scans, the model was built to further classify the types of COVID-19 based on CT images on admission
Summary
In December 2019, a cluster of patients with unidentified pneumonia disease was discovered. Soon a novel coronavirus was isolated from these patients, which belonged to the betacoronavirus family and was named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Zhu et al, 2020). Mild patients may have no obvious clinical symptoms, while severe patients may have dyspnea and hypoxemia. The main transmission route was via respiratory droplets and close contact (Li et al, 2021). Patients with COVID-19 but with no symptoms may have transmitted the virus to close contacts before a definite diagnosis could be made. Though there are many studies on identifying effective drugs against SARS-CoV-2 (Tang et al, 2020; Peng et al, 2021), most of them need further experimental and clinical validation (Peng et al, 2020; Zhou et al, 2020).
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