Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
    displaying 1 - 10 records in total 17
    records per page




    Surfactant Therapy for COVID-19 Related ARDS: A Retrospective Case-Control Pilot Study.

    Authors: Simone Piva; DiBlasi Robert; April E Slee; Alan H Jobe; Aldo M Roccaro; Matteo Filippini; Nicola Latronico; Michele Bertoni; John C Marshall; Michael A Portman

    doi:10.21203/rs.3.rs-78815/v1 Date: 2020-09-16 Source: ResearchSquare

    Background. COVID-19 causes acute respiratory distress HP respiratory distress MESHD syndrome ( ARDS MESHD) and depletes the lungs of surfactant, leading to prolonged mechanical ventilation and death MESHD. The feasibility and safety of surfactant delivery in COVID-19 ARDS MESHD patients have not been established. Methods. We performed retrospective analyses of data from patients receiving off-label use of natural surfactant during the COVID-19 pandemic.  Seven COVID-19 PCR positive ARDS MESHD patients received liquid Curosurf (720 mg) in 150 ml normal saline, divided into five 30 ml aliquots) and delivered via a bronchoscope into second-generation bronchi. Patients were matched with 14 comparable subjects receiving supportive care for ARDS during the same time period. Feasibility and safety were examined as well as the duration of mechanical ventilation and mortality. Results. Patients showed no evidence of acute decompensation following surfactant installation into minor bronchi and lung retention for up to 2 hours.  Cox regression showed a reduction of 28-days mortality within the surfactant group, though not significant. The surfactant did not increase the duration of ventilation, and health care providers did not convert to COVID-19 positive. Conclusions. Surfactant delivery through bronchoscopy at a dose of 720 mg in 150 ml normal saline is feasible and safe for COVID-19 ARDS MESHD patients and health care providers during the pandemic. Surfactant administration does not cause acute decompensation, and it could be related to improved survival and reduction of mechanical ventilation duration. This study supports the future performance SERO of randomized clinical trials evaluating the efficacy of meticulous surfactant delivery. 

    Risk Factors Analysis of COVID-19 Patients with ARDS MESHD and Prediction Based on Machine Learning

    Authors: Wan Xu; Nan-Nan Sun; Hai-Nv Gao; Zhi-Yuan Chen; Ya Yang; Bin Ju; Ling-Ling Tang

    doi:10.21203/rs.3.rs-77820/v1 Date: 2020-09-15 Source: ResearchSquare

    COVID-19 is a newly emerging infectious disease MESHD, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress HP respiratory distress MESHD syndrome ( ARDS MESHD) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS MESHD patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the two groups were elaborately compared and both traditional machine learning algorithms MESHD and deep learning-based methods were used to build the prediction models. Results indicated the median age TRANS of ARDS MESHD patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male TRANS and patients with BMI>25 were more likely to develop ARDS MESHD. The clinical features of ARDS MESHD patients included cough HP (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection MESHD (30.3%), and comorbidities such as hypertension HP hypertension MESHD (48.7%). Abnormal biochemical indicators such as lymphocyte count, leukocyte counting, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, sensitivity SERO, and specificity in identifying the mild patients who were easy to develop ARDS MESHD, which undoubtedly helps to optimize the treatment strategy, reduce mortality, and relieve the medical pressure. 

    Laboratory biomarkers associated with COVID-19 severity and management.

    Authors: Stephen Keddie; Oliver J Ziff; Michael KL Chou; Rachel L Taylor; Amanda Heslegrave; Edmund Garr; Neghat Lakdawala; Andrew Church; Dalia Ludwig; Jessica Manson; Marie Scully; Eleni Nastouli; Miles D Chapman; Melanie Hart; Michael P Lunn; Cristina M. Tato; Kevin K. Leung; Bryan Greenhouse; James A. Wells; Ainara Coduras Erdozain; Carmen Martinez Cilleros; Jose Loureiro Amigo; Francisco Epelde; Carlos Lumbreras Bermejo; Juan Miguel Anton Santos

    doi:10.1101/2020.08.18.20168807 Date: 2020-08-21 Source: medRxiv

    The heterogeneous disease course of COVID-19 is unpredictable, ranging from mild self-limiting symptoms to cytokine storms, acute respiratory distress syndrome MESHD respiratory distress HP syndrome ( ARDS MESHD), multi-organ failure MESHD and death MESHD. Identification of high-risk cases will enable appropriate intervention and escalation. This study investigates the routine laboratory tests and cytokines implicated in COVID-19 for their potential application as biomarkers of disease severity, respiratory failure HP respiratory failure MESHD and need of higher-level care. From analysis of 203 samples, CRP, IL-6, IL-10 and LDH were most strongly correlated with the WHO ordinal scale of illness severity, the fraction of inspired oxygen delivery, radiological evidence of ARDS MESHD and level of respiratory support (p[≤]0.001). IL-6 levels of >3.27pg/ml provide a sensitivity SERO of 0.87 and specificity of 0.64 for a requirement of ventilation, and a CRP of >37mg/L of 0.91 and 0.66. Reliable stratification of high-risk cases has significant implications on patient triage, resource management and potentially the initiation of novel therapies in severe patients.

    A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia HP pneumonia MESHD in China

    Authors: Dawei Yang; Tao Xu; Xun Wang; Deng Chen; Ziqiang Zhang; Lichuan Zhang; Jie Liu; Kui Xiao; Li Bai; Yong Zhang; Lin Zhao; Lin Tong; Chaomin Wu; Yaoli Wang; Chunling Dong; Maosong Ye; Yu Xu; Zhenju Song; Hong Chen; Jing Li; Jiwei Wang; Fei Tan; Hai Yu; Jian Zhou; Jinming Yu; Chunhua Du; Hongqing Zhao; Yu Shang; Linian Huang; Jianping Zhao; Yang Jin; Charles A. Powell; Yuanlin Song; Chunxue Bai

    doi:10.1101/2020.08.07.20163402 Date: 2020-08-11 Source: medRxiv

    Background The outbreak of coronavirus disease MESHD 2019 (COVID-19) has become a global pandemic acute infectious disease MESHD, especially with the features of possible asymptomatic TRANS carriers TRANS and high contagiousness. It causes acute respiratory distress HP respiratory distress MESHD syndrome and results in a high mortality rate if pneumonia HP is involved. Currently, it is difficult to quickly identify asymptomatic TRANS cases or COVID-19 patients with pneumonia HP pneumonia MESHD due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans, which facilitates the spread of the disease TRANS at the community level, and contributes to the overwhelming of medical resources in intensive care units. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist global frontline doctors to efficiently and precisely diagnose asymptomatic TRANS COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan Unversity (approval number B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. These patients had SARS-CoV-2 RT-PCR test results and chest CT scans, both of which were used as the gold standard for the diagnosis of COVID-19 and COVID-19 pneumonia HP pneumonia MESHD. In particular, the dataset included 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, and 122 asymptomatic TRANS cases who had positive RT-PCR test results, amongst whom 31 cases were diagnosed. We also integrated the function of a survey in nCapp to collect user feedback from frontline doctors. Findings We applied the statistical method of a multi-factor regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are fast and accessible: 'Residing or visiting history in epidemic regions', 'Exposure history to COVID-19 patient', 'Dry cough HP', ' Fatigue HP', 'Breathlessness', 'No body temperature decrease after antibiotic treatment', 'Fingertip blood SERO oxygen saturation<=93%', ' Lymphopenia HP Lymphopenia MESHD', and 'C-reactive protein (CRP) increased'. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). To ensure the sensitivity SERO of the model, we used a cutoff value of 0.09. The sensitivity SERO and specificity of the model were 98.0% (95% CI: 96.9%, 99.1%) and 17.3% (95% CI: 15.0%, 19.6%), respectively, in the training dataset, and 96.5% (95% CI: 95.1%, 98.0%) and 18.8% (95% CI: 16.4%, 21.2%), respectively, in the validation dataset. In the subset of the 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, the model predicted 132 cases, accounting for 96.4% (95% CI: 91.7%, 98.8%) of the cases. In the subset of the 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, the model predicted 59 cases, accounting for 95.2% (95% CI: 86.5%, 99.0%) of the cases. Considering the specificity of the model, we used a cutoff value of 0.32. The sensitivity SERO and specificity of the model were 83.5% (95% CI: 80.5%, 86.4%) and 83.2% (95% CI: 80.9%, 85.5%), respectively, in the training dataset, and 79.6% (95% CI: 76.4%, 82.8%) and 81.3% (95% CI: 78.9%, 83.7%), respectively, in the validation dataset, which is very close to the published AI model. The results of the online survey 'Questionnaire Star' showed that 90.9% of nCapp users in WeChat mini programs were 'satisfied' or 'very satisfied' with the tool. The WeChat mini program received a significantly higher satisfaction rate than other platforms, especially for 'availability and sharing convenience of the App' and 'fast speed of log-in and data entry'. Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic TRANS patients and avoid misdiagnoses of cases with false-negative RT-PCR results. These patients require timely isolation or close medical supervision. By applying the model, medical resources can be allocated more reasonably, and missed diagnoses can be reduced. In addition, further education and interaction among medical professionals can improve the diagnostic efficiency for COVID-19, thus avoiding the transmission TRANS of the disease from asymptomatic TRANS patients at the community level.

    Montelukast in Hospitalized Patients Diagnosed with COVID-19

    Authors: Ahsan Khan; Christian Misdary; Nikhil Yegya-Raman; Sinae Kim; Navaneeth Narayanan; Sheraz Siddiqui; Padmini Salgame; Jared Radbel; Frank De Groote; Carl Michel; Janice Mehnert; Caleb Hernandez; Thomas Braciale; Jyoti Malhotra; Michael A. Gentile; Salma K. Jabbour

    doi:10.21203/rs.3.rs-52430/v1 Date: 2020-08-01 Source: ResearchSquare

    Background Several therapeutic agents have been assessed for the treatment of COVID-19, but few approaches have been proven efficacious. Because leukotriene receptor antagonists such as montelukast have been shown to reduce both cytokine release and lung inflammation MESHD in preclinical models of viral influenza and acute respiratory distress syndrome MESHD respiratory distress HP syndrome, we hypothesized that therapy with montelukast would reduce clinical deterioration as measured by the COVID-19 Ordinal Scale.Methods We performed a retrospective analysis of COVID-19 confirmed hospitalized patients treated with or without montelukast. We used “clinical deterioration” as the primary endpoint, a binary outcome defined as any increase in the Ordinal Scale value from Day 1 to Day 3 of hospital stay, as these data were uniformly available for all admitted patients before hospital discharge. Rates of clinical deterioration between the montelukast and non-montelukast groups were compared using the Fisher’s exact test. Univariate logistic regression was also used to assess the association between montelukast use and clinical deterioration.Results A total of 92 patients were analyzed, 30 received montelukast at the discretion of the treating physician and 62 patients who did not receive montelukast. Patients receiving montelukast experienced significantly fewer events of clinical deterioration compared to patients not receiving montelukast (10% vs 32%, p = 0.022). Sensitivity SERO analysis among those without asthma HP showed a trend toward fewer clinical deterioration events in the montelukast group than non-montelukast groups (11% vs 33%, p = 0.077). Sensitivity SERO analysis among those who did not receive azithromycin showed fewer clinical deterioration events in the montelukast group vs. non-montelukast groups (8% vs 32%, p = 0.030).Conclusions Our findings suggest that montelukast associates with a reduction in clinical deterioration for COVID-19 confirmed patients as measured on the COVID-19 Ordinal Scale. Montelukast may have activity in COVID-19 infection MESHD, and future efforts should evaluate this potential therapy.

    Fighting COVID-19 spread among nursing home residents even in absence of molecular diagnosis: a retrospective cohort study.

    Authors: Alessio Strazzulla; Paul Tarteret; Maria Concetta Postorino; Marie Picque; Astrid de Pontfarcy; Nicolas Vignier; Catherine Chakvetadze; Coralie Noel; Cecile Drouin; Zine Eddine Benguerdi; Sylvain Diamantis

    doi:10.21203/rs.3.rs-51305/v1 Date: 2020-07-30 Source: ResearchSquare

    Background Access to molecular diagnosis was limited out-of-hospital in France during the 2020 coronavirus disease 2019 (COVID-19) epidemic. This study describes the evolution of COVID-19 outbreak in a nursing home in absence of molecular diagnosis. Methods A monocentric prospective study was conducted in a French nursing home from March 17th, 2020 to June 11th, 2020. Because of lack of molecular tests for severe acute respiratory syndrome 2 (SARS-Cov2) infection MESHD, probable COVID-19 cases were early identified considering only respiratory and not-respiratory symptoms and therefore preventing measures and treatments were enforced. Once available, serology tests were performed at the end of the study.A chronologic description of new cases and deaths MESHD was made together with a description of COVID-19 symptoms. Data about personal characteristics and treatments were collected and the following comparisons were performed: i) probable COVID-19 cases vs asymptomatic TRANS residents; ii) SARS-Cov2 seropositive residents vs seronegative residents. Results Overall, 32/66 (48.5%) residents and 19/39 (48.7%) members of health-care personnel were classified as probable COVID-19 cases. A total of 34/61 (55.7%) tested residents resulted seropositive. Death occurred in 4/66 (6%) residents. Diagnosis according to symptoms had 65% of sensitivity SERO, 78% of specificity, 79% of positive predictive value SERO and 64% of negative predictive value SERO.In resident population, the following symptoms were registered: 15/32 (46.8%) lymphopenia HP lymphopenia MESHD, 15/32 (46.8%) fever HP fever MESHD, 8/32 (25%) fatigue HP fatigue MESHD, 8/32 (25%) cough HP, 6/32 (18.8%) diarrhoea MESHD, 4/32 (12.5%) severe respiratory distress HP requiring oxygen therapy, 4/32 (12.5%) fall HP, 3/32 (9.4%) conjunctivitis HP conjunctivitis MESHD, 2/32 (6.3%) abnormal pulmonary noise at chest examination and 2/32 (6,25%) abdominal pain HP abdominal pain MESHD. Probable COVID-19 cases were older (81.3 vs 74.9; p=0.007) and they had higher prevalence SERO of atrial fibrillation HP atrial fibrillation MESHD (8/32, 25% vs 2/34, 12%; p=0.030); insulin treatment (4/34, 12% vs 0, 0%; p=0.033) and positive SARS-Cov2 serology (22/32, 69% vs 12/34, 35%; p=0.001) than asymptomatic TRANS residents. Seropositive residents had lower prevalence SERO of diabetes MESHD (4/34, 12% vs 9/27, 33%; p=0.041) and angiotensin-converting-enzyme inhibitors’ intake (1/34, 1% vs 5/27, 19%; p=0.042). Conclusions During SARS-Cov2 epidemic, early detection of respiratory and not-respiratory symptoms allowed to enforce extraordinary measures. They achieved limiting contagion and deaths among nursing home residents, even in absence of molecular diagnosis.

    Developing the nomogram for the prediction of in-hospital incidence of acute respiratory distress syndrome MESHD in patients with COVID-19

    Authors: Ning Ding; Yang Zhou; Guifang Yang; Cuirong Guo; Fengning Tang; Xiangping Chai

    doi:10.21203/rs.3.rs-49304/v1 Date: 2020-07-26 Source: ResearchSquare

    Background: Acute respiratory distress HP respiratory distress MESHD syndrome ( ARDS MESHD) was the most common complication of coronavirus disease-2019(COVID-19), leading to poor clinical outcomes. However, the model to predict the in-hospital incidence of ARDS MESHD in patients with COVID-19 is limited. Therefore, we aimed to develop a predictive nomogram for the in-hospital incidence of ARDS in COVID-19 patients.Methods: Patients with COVID-19 admitted to Changsha Public Health Centre between Jan 30, 2020, and Feb 22, 2020, were enrolled. Clinical characteristics and laboratory variables were analyzed in patients with ARDS. Risk factors for ARDS MESHD were selected by LASSO binary logistic regression. Nomogram was established based on risk factors and validated by the dataset.Results: A total of 113 patients, involving 99 in the non-ARDS group and 14 in the ARDS group were included in the study. 8 variables including hypertension HP hypertension MESHD, chronic obstructive pulmonary disease HP obstructive pulmonary disease MESHD ( COPD MESHD), cough HP cough MESHD, lactate dehydrogenase (LDH), creatine kinase (CK), white blood SERO count (WBC), body temperature, and heart rate were identified to be included in the model. The specificity, sensitivity SERO, and accuracy of the full model were 100%, 85.7%, and 87.5% respectively. The calibration curve also showed good agreement between the predicted and observed values in the model.Conclusions: The nomogram can predict the in-hospital incidence of ARDS in COVID-19 patients. It helps physicians to make an individualized treatment plan for each patient.

    Cumulative oxygen deficit is a novel biomarker for the timing of invasive mechanical ventilation in COVID-19 patients with respiratory distress HP: a time-dependent propensity score analysis

    Authors: Huiqing Ge; Jiancang Zhou; Fangfang Lv; Junli Zhang; Jun Yi; Changming Yang; Lingwei Zhang; Yuhan Zhou; Qing Pan; Zhongheng Zhang

    doi:10.21203/rs.3.rs-42597/v1 Date: 2020-07-13 Source: ResearchSquare

    Background and objectives: The timing of invasive mechanical ventilation (IMV) is controversial in COVID-19 patients with acute respiratory hypoxemia MESHD hypoxemia HP. The study aimed to develop a novel biomarker called cumulative oxygen deficit (COD) for the initiation of IMV.Methods: The study was conducted in four designated hospitals for treating COVID-19 patients in Jingmen, Wuhan, from January to March 2020. COD was defined to account for both the magnitude and duration of hypoxemia HP hypoxemia MESHD. A higher value of COD indicated more oxygen deficit. The predictive performance SERO of COD was calculated in multivariable Cox regression models. Time-dependent propensity score matching was performed to explore the effectiveness of IMV versus other non-invasive respiratory supports on survival outcome.Results: A number of 111 patients including 80 in the non-IMV group and 31 in the IMV group were included. Patients with IMV had significantly lower PaO2 (62 (49, 89) vs. 90.5 (68, 125.25) mmHg; p < 0.001), and higher COD (-6.87 (-29.36, 52.38) vs. -231.68 (-1040.78, 119.83)) than patients without IMV. As compared to patients with COD < 0, patients with COD > 30 had higher risk of fatality (HR: 3.79, 95% CI: 2.57 to 16.93; p = 0.037) , and those with COD > 50 were 10 times more likely to die (HR: 10.45, 95% CI: 1.28 to 85.37; p = 0.029). The Cox regression model performed in the time-dependent propensity score matched cohort showed that IMV was associated with half of the hazard of death MESHD than those without IMV (HR: 0.56; 95% CI: 0.16 to 1.93; p = 0.358).Conclusions: The study developed a novel biomarker COD which considered both magnitude and duration of hypoxemia HP hypoxemia MESHD, to assist the timing of IMV in patients with COVID-19. We suggest IMV should be the preferred ventilatory support once the COD reaches 30.

    Early fibroproliferative changes on high-resolution CT predict mortality in COVID-19 pneumonia HP pneumonia MESHD patients with ARDS MESHD

    Authors: Zhilin Zeng; Min Xiang; Hanxiong Guan; Yiwen liu; huilan Zhang; Liming Xia; Zhan Juan; Qiongjie Hu

    doi:10.21203/rs.3.rs-32245/v1 Date: 2020-05-28 Source: ResearchSquare

    Objectives: To investigate the chest high-resolution CT (HRCT) findings in coronavirus disease MESHD 2019 (COVID-19) pneumonia HP pneumonia MESHD patients with acute respiratory distress syndrome MESHD respiratory distress HP syndrome ( ARDS MESHD) and to evaluate its relationship with clinical outcome.Materials and Methods In this retrospective study, seventy-nine COVID-19 patients with ARDS MESHD were recruited. Clinical data were extracted from electronic medical records and analyzed. HRCT scans, obtained within 3 days before clinical ARDS MESHD onset, were evaluated by three independent observers and graded into six findings according to the extent of fibroproliferation. Multivariable Cox proportional hazard regression analysis was used to assess the independent predictive value of the CT score and radiologically fibroproliferation. Patient survival was determined by Kaplan-Meier analysis.Results: Compared with survivors, non-survivors showed higher of lung fibroproliferation, whereas there no significant differences in the area of increased attenuation without traction bronchiolectasis HP or bronchiectasis HP. A HRCT score <230 enabled prediction of survival with 73.5% sensitivity SERO and 93.3% specificity (AUC= 0.9; 95% CI 0.831 to 0.968). Multivariate Cox proportional hazards model showed that the HRCT score is a significant independent risk factor for mortality (HR 13.007; 95% CI 3.935 to 43.001). Kaplan-Meier analysis revealed HRCT score≥230 was associated with higher fatality rate. Organ injury occurred less frequently in patients with HRCT score<230 compared to those with HRCT score≥230.Conclusion: Early pulmonary fibroproliferative changes in HRCT predicts increased mortality and susceptibility to organ injury in COVID-19 pneumonia HP pneumonia MESHD patients with early ARDS MESHD.

    ROBUST COVID-19-RELATED CONDITION CLASSIFICATION NETWORK

    Authors: Maximiliano Lucius; Martin Belvisi; Carlos Maria Galmarini

    doi:10.1101/2020.05.19.20106336 Date: 2020-05-26 Source: medRxiv

    COVID-19 can exponentially precipitate life-threatening emergencies as witnessed during the recent spreading of a novel coronavirus infection MESHD which can rapidly evolve into lung collapse and respiratory distress HP (among other various severe clinical conditions). Our study evaluates the performance SERO of a tailor-designed deep convolutional network on the tasks of early detection and localization of radiological signs associated to COVID-19 on frontal chest X-rays. We also asses the frameworks capacity in differentiating the above-mentioned signs, which are usually confused with the more usual common bacterial and viral pneumonias HP pneumonias MESHD. Open-source chest X-ray images categorized as Normal, Non-COVID-19 and COVID-19 pneumonias HP pneumonias MESHD were downloaded from the NIH (n=2,259), RSNA (n=600) and HM Hospitales (n=2,307). Our algorithmic framework was able to precisely detect the images with COVID19- related radiological findings (mean Accuracy: 90.5%; Sensitivity SERO: 80.6%; Specificity: 98.0%), whilst correctly categorizing images deemed as Non-COVID-19 pneumonias HP pneumonias MESHD (mean Accuracy: 88.4%; Sensitivity SERO: 93.3%; Specificity: 92.0%) and normal chest X- rays (mean Accuracy 92.1%; Sensitivity SERO: 91.8%; Specificity: 94.3%). The associated results show that our AI framework is able to classify COVID-19 accurately, making of it a potential tool to improve the diagnostic performance SERO across primary-care centres and, to grant priority to a subset of algorithmic selected images for urgent follow-on expert review. This would sensibly accelerate diagnosis in remote locations, reduce the bottleneck on specialized centres, and/or help to alleviate the needs on situations of scarcity in the availability of molecular tests.

The ZB MED preprint Viewer preVIEW includes all COVID-19 related preprints from medRxiv and bioRxiv, from ChemRxiv, from ResearchSquare, from arXiv and from Preprints.org and is updated on a daily basis (7am CET/CEST).
The web page can also be accessed via API.

Sources


Annotations

All
None
MeSH Disease
Human Phenotype
Transmission
Seroprevalence


Export subcorpus as...

This service is developed in the project nfdi4health task force covid-19 which is a part of nfdi4health.

nfdi4health is one of the funded consortia of the National Research Data Infrastructure programme of the DFG.