Coronavirus disease 2019 brings a huge burden on the medical industry all over the world.In the background of artificial intelligence(AI)and Internet of Things(IoT)technologies,chest computed tomography(CT)and chest X...Coronavirus disease 2019 brings a huge burden on the medical industry all over the world.In the background of artificial intelligence(AI)and Internet of Things(IoT)technologies,chest computed tomography(CT)and chest Xray(CXR)scans are becoming more intelligent,and playing an increasingly vital role in the diagnosis and treatment of diseases.This paper will introduce the segmentation of methods and applications.CXR and CT diagnosis of COVID-19 based on deep learning,which can be widely used to fight against COVID-19.展开更多
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 20...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.展开更多
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 intelligenc...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.展开更多
基金supported by the Open Project of State Key Laboratory of Millimeter Wave,Southeast University,China,under Grant K202218.
文摘Coronavirus disease 2019 brings a huge burden on the medical industry all over the world.In the background of artificial intelligence(AI)and Internet of Things(IoT)technologies,chest computed tomography(CT)and chest Xray(CXR)scans are becoming more intelligent,and playing an increasingly vital role in the diagnosis and treatment of diseases.This paper will introduce the segmentation of methods and applications.CXR and CT diagnosis of COVID-19 based on deep learning,which can be widely used to fight against COVID-19.
基金Science and Technology Support of Key R&D Plan of Ningxia Autonomous Region“Novel Coronavirus Pneumonia Prevention and Control”special Project(Project No.2020BEG03057,2020BEG03058)。
文摘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.
文摘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.
