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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
2.
Stem Cell Res Ther ; 11(1): 207, 2020 05 27.
Article in English | MEDLINE | ID: covidwho-381721

ABSTRACT

The novel coronavirus disease 2019 (COVID-19) has grown to be a global public health emergency since patients were first detected in Wuhan, China. Thus far, no specific drugs or vaccines are available to cure the patients with COVID-19 infection. The immune system and inflammation are proposed to play a central role in COVID-19 pathogenesis. Mesenchymal stem cells (MSCs) have been shown to possess a comprehensive powerful immunomodulatory function. Intravenous infusion of MSCs has shown promising results in COVID-19 treatment. Here, we report a case of a severe COVID-19 patient treated with human umbilical cord Wharton's jelly-derived MSCs (hWJCs) from a healthy donor in Liaocheng People's Hospital, China, from February 24, 2020. The pulmonary function and symptoms of the patient with COVID-19 pneumonia was significantly improved in 2 days after hWJC transplantation, and recovered and discharged in 7 days after treatment. After treatment, the percentage and counts of lymphocyte subsets (CD3+, CD4+, and CD8+ T cell) were increased, and the level of IL-6, TNF-α, and C-reactive protein is significantly decreased after hWJC treatment. Thus, the intravenous transplantation of hWJCs was safe and effective for the treatment of patients with COVID-19 pneumonia, especially for the patients in a critically severe condition. This report highlights the potential of hWJC infusions as an effective treatment for COVID-19 pneumonia.


Subject(s)
Betacoronavirus/pathogenicity , Coronavirus Infections/therapy , Mesenchymal Stem Cell Transplantation/methods , Mesenchymal Stem Cells/cytology , Pneumonia, Viral/therapy , Betacoronavirus/genetics , C-Reactive Protein/immunology , C-Reactive Protein/metabolism , Clinical Laboratory Techniques , Coronavirus Infections/diagnosis , Coronavirus Infections/drug therapy , Coronavirus Infections/immunology , Humans , Immunomodulation , Infusions, Intravenous , Interleukin-6/blood , Interleukin-6/immunology , Lymphocyte Subsets/immunology , Lymphocyte Subsets/virology , Male , Mesenchymal Stem Cells/immunology , Middle Aged , Pandemics , Pneumonia, Viral/diagnosis , Pneumonia, Viral/immunology , Treatment Outcome , Tumor Necrosis Factor-alpha/blood , Tumor Necrosis Factor-alpha/immunology , Umbilical Cord/cytology , Umbilical Cord/immunology , Wharton Jelly/cytology , Wharton Jelly/immunology
3.
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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