Analysis of Clinical and Imaging Characteristics of Suspected and Confirmed COVID-19 Patients

Objective:To analyze the clinical characteristics and chest CT imaging characteristics of patients with confirmed COVID-19(COVID-19)and patients with suspected COVID-19.Methods:The study time span was from February 2020 to May 2020.The case samples were selected from 72 patients with confirmed covid-19 and suspected covid-19 diagnosed and treated by The First People’s Hospital of Yinchuan and Yinchuan Temporary Emergency Hospital,including 38 patients with confirmed covid-19 and 34 patients with suspected covid-19.All patients underwent laboratory examination and chest CT examination,and the specific examination results were compared and analyzed.Results:There were significant differences in number of white blood cell,percentage of lymphocytes,creatine kinase and erythrocyte sedimentation rate between confirmed and suspected COVID-19 patients(P<0.05).The CT imaging characteristics of COVID-19 patients were compared with those of suspected COVID-19 patients.The lesions of COVID-19 patients were mostly characterized by mixed ground glass density and pure ground glass density.There were vascular thickening and interstitial thickness increase,and accompanied by bronchiectasis or air bronchogram.The distribution of lesions was mostly subpleural without pleural effusion.The lesion area of suspected COVID-19 patients mostly showed solid density and mixed ground glass density.The lesion was distributed along bronchovascular and pleural effusion was observed.Conclusion:There are some differences in biochemical indexes and chest CT images between confirmed and suspected covid-19 patients,which can be used for differential diagnosis.

Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19

Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients.This study aimed to develop a COVID-19 diagnosis and prediction(AIMDP)model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography(CT)scans.The proposed system uses convolutional neural networks(CNNs)as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment.We employed the whale optimization algorithm(WOA)to select the most relevant patient signs.A set of experiments validated AIMDP performance.It demonstrated the superiority of AIMDP in terms of the area under the curve-receiver operating characteristic(AUC-ROC)curve,positive predictive value(PPV),negative predictive rate(NPR)and negative predictive value(NPV).AIMDP was applied to a dataset of hundreds of real data and CT images,and it was found to achieve 96%AUC for diagnosing COVID-19 and 98%for overall accuracy.The results showed the promising performance of AIMDP for diagnosing COVID-19 when compared to other recent diagnosing and predicting models.

A Systematic Literature Review of Deep Learning Algorithms for Segmentation of the COVID-19 Infection

Coronavirus has infected more than 753 million people,ranging in severity from one person to another,where more than six million infected people died worldwide.Computer-aided diagnostic(CAD)with artificial intelligence(AI)showed outstanding performance in effectively diagnosing this virus in real-time.Computed tomography is a complementary diagnostic tool to clarify the damage of COVID-19 in the lungs even before symptoms appear in patients.This paper conducts a systematic literature review of deep learning methods for classifying the segmentation of COVID-19 infection in the lungs.We used the methodology of systematic reviews and meta-analyses(PRISMA)flow method.This research aims to systematically analyze the supervised deep learning methods,open resource datasets,data augmentation methods,and loss functions used for various segment shapes of COVID-19 infection from computerized tomography(CT)chest images.We have selected 56 primary studies relevant to the topic of the paper.We have compared different aspects of the algorithms used to segment infected areas in the CT images.Limitations to deep learning in the segmentation of infected areas still need to be developed to predict smaller regions of infection at the beginning of their appearance.