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1.
Radiology ; 296(2): E65-E71, 2020 08.
Article in English | MEDLINE | ID: covidwho-657750

ABSTRACT

Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performance. Materials and Methods In this retrospective and multicenter study, a deep learning model, the COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT scans for the detection of COVID-19. CT scans of community-acquired pneumonia (CAP) and other non-pneumonia abnormalities were included to test the robustness of the model. The datasets were collected from six hospitals between August 2016 and February 2020. Diagnostic performance was assessed with the area under the receiver operating characteristic curve, sensitivity, and specificity. Results The collected dataset consisted of 4352 chest CT scans from 3322 patients. The average patient age (±standard deviation) was 49 years ± 15, and there were slightly more men than women (1838 vs 1484, respectively; P = .29). The per-scan sensitivity and specificity for detecting COVID-19 in the independent test set was 90% (95% confidence interval [CI]: 83%, 94%; 114 of 127 scans) and 96% (95% CI: 93%, 98%; 294 of 307 scans), respectively, with an area under the receiver operating characteristic curve of 0.96 (P < .001). The per-scan sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175 scans) and 92% (239 of 259 scans), respectively, with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.93, 0.97). Conclusion A deep learning model can accurately detect coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions. © RSNA, 2020 Online supplemental material is available for this article.


Subject(s)
Artificial Intelligence , Betacoronavirus , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Adult , Aged , Clinical Laboratory Techniques/methods , Community-Acquired Infections/diagnostic imaging , Coronavirus Infections/diagnosis , Deep Learning , Diagnosis, Differential , Female , Humans , Imaging, Three-Dimensional/methods , Male , Middle Aged , Pandemics , ROC Curve , Radiographic Image Interpretation, Computer-Assisted/methods , Retrospective Studies , Sensitivity and Specificity , Tomography, X-Ray Computed/methods
3.
SN Compr. Clin. Med. ; 2020.
Article in English | ELSEVIER | ID: covidwho-343435

ABSTRACT

COVID-19 is currently a pandemic in the world, can invade multiple systems, and has a high morbidity and mortality. So far, no cases of acute cerebrovascular disease have been reported. This article reports the clinical features of a COVID-19 patient whose first symptom was cerebral hemorrhage. More importantly, after the craniotomy, the patient had high fever and it was difficult to retreat. After cerebrospinal fluid testing, it was determined that an intracranial infection had occurred. After anti-infection and plasma infusion of the recovered person, the patient’s symptoms gradually improved. This case suggests that COVID-19 may infringe on cerebral blood vessels and cause cerebral hemorrhage. Transfusion of plasma from rehabilitation patients is effective for critically ill patients.

4.
BMC Med ; 18(1): 123, 2020 05 06.
Article in English | MEDLINE | ID: covidwho-176083

ABSTRACT

BACKGROUND: Compared to adults, there are relatively few studies on COVID-19 infection in children, and even less focusing on the unique features of COVID-19 in children in terms of laboratory findings, locations of computerized tomography (CT) lesions, and the role of CT in evaluating clinical recovery. The objective of this study is to report the results from patients at Wuhan Children's Hospital, located within the initial center of the outbreak. METHODS: Clinical, imaging, and laboratory data of 76 children were collected retrospectively and analyzed with the Fisher exact test and Cox regression statistical methods. RESULTS: Among 50 children with a positive COVID-19 real-time reverse-transcriptase polymerase chain reaction (PCR), five had negative PCR results initially but showed positive results in subsequent tests. Eight (16%) patients had lymphopenia, seven (14%) with thrombocytopenia, four (8%) with lymphocytosis, two (4%) with thrombocytosis, ten (20%) with elevated C-reactive protein, four (8%) with hemoglobin above, and six (12%) with below standard reference values. Seven (14%) of the 50 had no radiologic evidence of disease on chest CT. For the 43 patients who had abnormal CT findings, in addition to previously reported patterns of ground-glass opacity (67%), local patchy shadowing (37%), local bilateral patchy shadowing (21%), and lesion location of lower lobes (65%), other CT features include that an overwhelming number of pediatric patients had lesions in the subpleural area (95%) and 22 of the 28 lower lobe lesions were in the posterior segment (78%). Lesions in most of the 15 patients (67%) who received chest CT at discharge were not completely absorbed, and 26% of these pediatric patients had CT lesions that were either unchanged or worse. CONCLUSIONS: There were a few differences between COVID-19 children and COVID-19 adults in terms of laboratory findings and CT characteristics. CT is a powerful tool to detect and characterize COVID-19 pneumonia but has little utility in evaluating clinical recovery for children. These results oppose current COVID-19 hospital discharge criteria in China, as one requirement is that pulmonary imaging must show significant lesion absorption prior to discharge. These differences between pediatric and adult cases of COVID-19 may necessitate pediatric-specific discharge criteria.


Subject(s)
Coronavirus Infections/diagnosis , Pneumonia, Viral/diagnosis , Betacoronavirus , Child , Child, Preschool , China , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/therapy , Disease Outbreaks , Female , Humans , Lung/diagnostic imaging , Male , Pandemics , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/therapy , Real-Time Polymerase Chain Reaction , Reference Values , Retrospective Studies , Tomography, X-Ray Computed/methods
6.
Radiology ; 296(2): E65-E71, 2020 08.
Article in English | MEDLINE | ID: covidwho-10509

ABSTRACT

Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performance. Materials and Methods In this retrospective and multicenter study, a deep learning model, the COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT scans for the detection of COVID-19. CT scans of community-acquired pneumonia (CAP) and other non-pneumonia abnormalities were included to test the robustness of the model. The datasets were collected from six hospitals between August 2016 and February 2020. Diagnostic performance was assessed with the area under the receiver operating characteristic curve, sensitivity, and specificity. Results The collected dataset consisted of 4352 chest CT scans from 3322 patients. The average patient age (±standard deviation) was 49 years ± 15, and there were slightly more men than women (1838 vs 1484, respectively; P = .29). The per-scan sensitivity and specificity for detecting COVID-19 in the independent test set was 90% (95% confidence interval [CI]: 83%, 94%; 114 of 127 scans) and 96% (95% CI: 93%, 98%; 294 of 307 scans), respectively, with an area under the receiver operating characteristic curve of 0.96 (P < .001). The per-scan sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175 scans) and 92% (239 of 259 scans), respectively, with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.93, 0.97). Conclusion A deep learning model can accurately detect coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions. © RSNA, 2020 Online supplemental material is available for this article.


Subject(s)
Artificial Intelligence , Betacoronavirus , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Adult , Aged , Clinical Laboratory Techniques/methods , Community-Acquired Infections/diagnostic imaging , Coronavirus Infections/diagnosis , Deep Learning , Diagnosis, Differential , Female , Humans , Imaging, Three-Dimensional/methods , Male , Middle Aged , Pandemics , ROC Curve , Radiographic Image Interpretation, Computer-Assisted/methods , Retrospective Studies , Sensitivity and Specificity , Tomography, X-Ray Computed/methods
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