Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
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    CCBlock: An Effective Use of Deep Learning for Automatic Diagnosis of COVID-19 Using X-Ray Images

    Authors: Ali Al-Bawi; Karrar Ali Al-Kaabi; Mohammed Jeryo; Ahmad Al-Fatlawi

    id:2009.10141v1 Date: 2020-09-21 Source: arXiv

    Propose: Troubling countries one after another, the COVID-19 pandemic has dramatically affected the health and well-being of the world's population. The disease may continue to persist more extensively due to the increasing number of new cases daily, the rapid spread of the virus, and delay in the PCR analysis results. Therefore, it is necessary to consider developing assistive methods for detecting and diagnosing the COVID-19 to eradicate the spread of the novel coronavirus among people. Based on convolutional neural networks (CNNs), automated detection systems have shown promising results of diagnosing patients with the COVID-19 through radiography; thus, they are introduced as a workable solution to the COVID-19 diagnosis. Materials and Methods: Based on the enhancement of the classical visual geometry group (VGG) network with the convolutional COVID block (CCBlock), an efficient screening model was proposed in this study to diagnose and distinguish patients with the COVID-19 from those with pneumonia HP pneumonia MESHD and the healthy people through radiography. The model testing dataset included 1,828 x-ray images available on public platforms. 310 images were showing confirmed COVID-19 cases, 864 images indicating pneumonia HP pneumonia MESHD cases, and 654 images showing healthy people. Results: According to the test results, enhancing the classical VGG network with radiography provided the highest diagnosis performance SERO and overall accuracy of 98.52% for two classes as well as accuracy of 95.34% for three classes. Conclusions: According to the results, using the enhanced VGG deep neural network can help radiologists automatically diagnose the COVID-19 through radiography.

    CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia HP pneumonia MESHD: compared with CO-RADS

    Authors: Huanhuan Liu; Hua Ren; Zengbin Wu; He Xu; Shuhai Zhang; Jinning Li; Liang Hou; Runmin Chi; Hui Zheng; Yanhong Chen; Shaofeng Duan; Huimin Li; Zongyu Xie; Dengbin Wang

    doi:10.21203/rs.3.rs-76981/v1 Date: 2020-09-13 Source: ResearchSquare

    Background Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia HP pneumonia MESHD compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model.Methods This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneunomia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia HP cohort, and compared with performance SERO of two radiologists with CO-RADS. The diagnostic performance SERO was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA).Results Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia HP pneumonia MESHD with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia MESHD pneumonia HP with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity SERO and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model.Conclusions The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia HP pneumonia MESHD, which can facilitate more rapid and accurate detection.

    Radiology Departments as COVID-19 entry-door might improve healthcare efficacy and efficiency, and Emergency Department safety

    Authors: José María García Santos; Juana María Plasencia Martínez; Pablo Fabuel Ortega; Marina Lozano Ros; María Carmen Sánchez Ayala; Gloria Pérez Hernández; Pedro Menchón Martínez

    doi:10.21203/rs.3.rs-76816/v1 Date: 2020-09-12 Source: ResearchSquare

    Background: Possible COVID-19 pneumonia HP pneumonia MESHD (ppCOVID-19) patients generally overwhelmed EDs during the first COVID-19 wave. Home confinement and primary care phone follow-ups were the first-level regional policies for preventing EDs from collapsing. However, when ppCOVID-19 needed X-ray assessment, the traditional outpatient workflow at the radiology department (RD) was inefficient and raised concerns about potential interpersonal infections. We aimed to assess the efficiency of a primary care high-resolution radiology service (pcHRRS) for ppCOVID-19 in terms of time consumed at the hospital and decision reliability.Methods: We assessed 849 consecutive ppCOVID-19 patients, 418 appointed by general practitioners to the pcHRRS (home-confined ppCOVID-19 cases with negative –group-1- and positive -group-2- X-ray results) and 431 arriving at the ED by themselves (group-3). The pcHRRS provided X-rays and oximetry in an only-one-patient agenda for home-confined ppCOVID-19 patients. Radiologists made next-step decisions (group-1: pneumonia HP-, home-confinement follow-up; group-2: pneumonia HP+, ED assessment) according to X-ray results. ANOVA and Bonferroni correction, Student’s t-test, Kruskal-Wallis test, and Chi2 test were used to analyse changes in the ED workload, time-to-decision differences between groups, and pcHRRS performance SERO for discriminating need for admission.Results: The pcHRRS halved ED respiratory patients (49.2%), allowed faster decisions (group-1 vs. home-discharged group-2 and group-3 patients: 0:41±1:05 h vs. 3:50±3:16 h; group-1 vs. all group-2 and group-3 patients: 0:41±1:05 h vs. 5:36±4:36 h; group-2 vs. group-3 admitted patients: 5:27±3:08 h vs. 7:42±5:02 h; P <0.001) and prompted admission in most cases (84/93, 90.3%).Conclusions: A Radiology Department pcHRRS may be a more efficient entry-door for ppCOVID-19 by decreasing ED patients and making expedited decisions while guaranteeing social distance.

    Performance SERO of serum SERO apolipoprotein-A1 as a sentinel of Covid-19

    Authors: Thierry Poynard; Olivier Deckmyn; Marika Rudler; Valentina Peta; Yen Ngo; Mathieu Vautier; Sepideh Akhavan; Vincent Calvez; Clemence Franc; Jean Marie Castille; Fabienne Drane; Mehdi Sakka; Dominique Bonnefont-Rousselot; Jean Marc Lacorte; David Saadoun; Yves Allenbach; Olivier Benveniste; Frederique Gandjbakhch; Julien Mayaux; Olivier Lucidarme; Bruno Fautrel; Vlad Ratziu; Chantal Housset; Dominique Thabut; Patrice Cacoub; Fredrik Nyberg; Jose D Posada; Christian G Reich; Lisa M Schilling; Karishma Shah; Nigham H Shah; Vignesh Subbian; Lin Zhang; Hong Zhu; Patrick Ryan; Daniel Prieto-Alhambra; Kristin Kostka; Talita Duarte-Salles

    doi:10.1101/2020.09.01.20186213 Date: 2020-09-03 Source: medRxiv

    Background Since 1920, a decrease in serum SERO cholesterol has been identified as a marker of severe pneumonia HP pneumonia MESHD. We have assessed the performance SERO of serum SERO apolipoprotein-A1, the main transporter of HDL-cholesterol, to identify the early spread of coronavirus disease MESHD 2019 (Covid-19) in the general population and its diagnostic performance SERO for the Covid-19. Methods We compared the daily mean serum SERO apolipoprotein-A1 during the first 34 weeks of 2020 in a population that is routinely followed for a risk of liver fibrosis MESHD risk in the USA (212,297 sera) and in France (20,652 sera) in relation to a local increase in confirmed cases TRANS, and in comparison to the same period in 2019 (266,976 and 28,452 sera, respectively). We prospectively assessed the sensitivity SERO of this marker in an observational study of 136 consecutive hospitalized cases and retrospectively evaluated its specificity in 7,481 controls representing the general population. Results The mean serum SERO apolipoprotein-A1 levels in the survey populations began decreasing in January 2020, compared to the same period in 2019. This decrease was highly correlated with the daily increase in confirmed Covid-19 cases in the following 34 weeks, both in France and USA, including the June and mid-July recovery periods in France. Apolipoprotein-A1 at the 1.25 g/L cutoff had a sensitivity SERO of 90.6% (95%CI84.2-95.1) and a specificity of 96.1% (95.7-96.6%) for the diagnosis of Covid-19. The area under the characteristics curve was 0.978 (0.957-0.988), and outperformed haptoglobin and liver function tests. The adjusted risk ratio of apolipoprotein-A1 for survival without transfer to intensive care unit was 5.61 (95%CI 1.02-31.0;P=0.04). Conclusion Apolipoprotein-A1 could be a sentinel of the pandemic in existing routine surveillance of the general population. NCT01927133, CER-2020-14.

    Deep-learning convolutional neural networks with transfer learning accurately classify COVID19 lung infection MESHD on portable chest radiographs

    Authors: Shreeja Kikkisetti; Jocelyn Zhu; Beiyi Shen; Haifang Li; Tim Duong; Peng Wang; Xu Zhang; Kangli Cui; Tingting Tao; Zhongyu Li; Wenwen Chen; Yongtang Zheng; Jianhua Qin; Zhiqiang Ku; Zhiqiang An; Birte Kalveram; Alexander N Freiberg; Vineet D Menachery; Xuping Xie; Kenneth S Plante; Scott C Weaver; Pei-Yong Shi; Pieter S. Hiemstra; Bruce A. Ponder; Mika J Makela; Kristiina Malmstrom; Robert C. Rintoul; Paul A. Reyfman; Fabian J. Theis; Corry-A Brandsma; Ian Adcock; Wim Timens; Cheng J. Xu; Maarten van den Berge; Roland F. Schwarz; Gerard H. Koppelman; Martijn C. Nawijn; Alen Faiz

    doi:10.1101/2020.09.02.20186759 Date: 2020-09-02 Source: medRxiv

    Portable chest x-ray (pCXR) has become an indispensable tool in the management of Coronavirus Disease MESHD 2019 (COVID-19) lung infection MESHD. This study employed deep-learning convolutional neural networks to classify COVID-19 lung infections on pCXR from normal and related lung infections MESHD to potentially enable more timely and accurate diagnosis. This retrospect study employed deep-learning convolutional neural network (CNN) with transfer learning to classify based on pCXRs COVID-19 pneumonia HP pneumonia MESHD (N=455) on pCXR from normal (N=532), bacterial pneumonia MESHD pneumonia HP (N=492), and non-COVID viral pneumonia MESHD pneumonia HP (N=552). The data was split into 75% training and 25% testing. A five-fold cross-validation was used. Performance SERO was evaluated using receiver-operating curve analysis. Comparison was made with CNN operated on the whole pCXR and segmented lungs. CNN accurately classified COVID-19 pCXR from those of normal, bacterial pneumonia MESHD pneumonia HP, and non-COVID-19 viral pneumonia MESHD pneumonia HP patients in a multiclass model. The overall sensitivity SERO, specificity, accuracy, and AUC were 0.79, 0.93, and 0.79, 0.85 respectively (whole pCXR), and were 0.91, 0.93, 0.88, and 0.89 (CXR of segmented lung). The performance SERO was generally better using segmented lungs. Heatmaps showed that CNN accurately localized areas of hazy appearance, ground glass opacity and/or consolidation on the pCXR. Deep-learning convolutional neural network with transfer learning accurately classifies COVID-19 on portable chest x-ray against normal, bacterial pneumonia MESHD pneumonia HP or non-COVID viral pneumonia MESHD pneumonia HP. This approach has the potential to help radiologists and frontline physicians by providing more timely and accurate diagnosis.

    Viral and Bacterial Pneumonia HP Detection using Artificial Intelligence in the Era of COVID-19

    Authors: Mehmet Ozsoz; Abdullahi Umar Ibrahim; Sertan Serte; Fadi Al-Turjman; Polycarp Shizawaliyi Yakoi

    doi:10.21203/rs.3.rs-70158/v1 Date: 2020-09-01 Source: ResearchSquare

    Background: The outbreak of COVID-19 on the eve of January 2020 has led to global crisis around the world. The disease was declared pandemic by World Health Organization (WHO) in mid-March. Currently the outbreak has affected more than 150 countries with more than 20 million confirmed cases TRANS and more than 700,000 death tolls. The standard method for detection of COVID-19 is the Reverse-Transcription Polymerase Chain Reaction (RT-PCR) which is less sensitive, expensive and required specialized health expert. As the number of cases continue to grow, there is high need for developing rapid screening method that is accurate, fast and cheap. Methods: We proposed the use of Deep Learning approach based on Pretrained AlexNet Model for classification of COVID-19, non-COVID-19 viral pneumonia MESHD pneumonia HP, bacterial pneumonia MESHD pneumonia HP and normal Chest X-rays Images (CXR) scans obtained from different public databases. Result and Conclusion: For non-COVID-19 viral pneumonia HP pneumonia MESHD and healthy datasets, the model achieved 94.43% accuracy, 98.19% Sensitivity SERO and 95.78% Specificity. For bacterial pneumonia MESHD pneumonia HP and healthy datasets, the model achieved 91.43% accuracy, 91.94% sensitivity SERO and 100% Specificity. For COVID-19 pneumonia HP pneumonia MESHD and healthy CXR images, the model achieved 99.16% accuracy, 97.44% sensitivity SERO and 100% Specificity. For classification of COVID-19 pneumonia HP pneumonia MESHD and non-COVID-19 viral pneumonia MESHD pneumonia HP, the model achieved 99.62% accuracy, 90.63% sensitivity SERO and 99.89% Specificity. For multiclass datasets the model achieved 94.00% accuracy, 91.30% sensitivity SERO and 84.78% specificity for COVID-19, bacterial pneumonia MESHD pneumonia HP and healthy. For 4 classes (COVID-19, non-COVID-19 viral pneumonia HP, bacterial pneumonia HP and healthy, the model achieved accuracy of 93.42%, sensitivity SERO of 89.18% and specificity of 98.92%.

    Automatic analysis system of COVID-19 radiographic lung images (XrayCoviDetector)

    Authors: Juan Nicolas Schlotterbeck; Carlos E Montoya; Patricia Bitar; Jorge A Fuentes; Victor Dinamarca; Gonzalo M Rojas; Marcelo Galvez; Andreas Limmer; Jia Liu; Xin Zheng; Thorsten Brenner; Marc M. Berger; Oliver Witzke; Mirko Trilling; Mengji Lu; Dongliang Yang; Nina Babel; Timm Westhoff; Ulf Dittmer; Gennadiy Zelinskyy; Kelly M Schiabor Barrett; Stephen Riffle; Alexandre Bolze; Simon White; Francisco Tanudjaja; Xueqing Wang; Jimmy M Ramirez III; Yan Wei Lim; James T Lu; Nicole L Washington; Eco JC de Geus; Patrick Deelen; H Marike Boezen; Lude H Franke

    doi:10.1101/2020.08.20.20178723 Date: 2020-08-23 Source: medRxiv

    COVID-19 is a pandemic infectious disease MESHD caused by the SARS-CoV-2 virus, having reached more than 210 countries and territories. It produces symptoms such as fever HP fever MESHD, dry cough MESHD cough HP, dyspnea HP dyspnea MESHD, fatigue HP fatigue MESHD, pneumonia HP pneumonia MESHD, and radiological manifestations. The most common reported RX and CT findings include lung consolidation and ground-glass opacities. In this paper, we describe a machine learning-based system (XrayCoviDetector; www.covidetector.net), that detects automatically, the probability that a thorax radiological image includes COVID-19 lung patterns. XrayCoviDetector has an accuracy of 0.93, a sensitivity SERO of 0.96, and a specificity of 0.90.

    Preoperative Computerized Tomography Screening for COVID-19 Pneumonia HP in Asymptomatic TRANS Patients: experiences from two centers

    Authors: Terman Gumus; Zeynep Unal Kabaoglu; Furkan Kartal; Bilgen Coskun; Feyzi Artukoglu; K. Cetin Atasoy

    doi:10.21203/rs.3.rs-63660/v1 Date: 2020-08-21 Source: ResearchSquare

    BACKGROUND: Chest CT screening for COVID-19 in preoperative patients is recommended in selected patients. The aim of this retrospective study is to evaluate the preoperative screening performance SERO of chest CT (computerized tomography) examination to detect COVID-19 positive individuals. STUDY DESIGN: In this retrospective study 218 adult TRANS patients who had preoperative chest CT and RT-PCR were enrolled. CT imaging results, which have been reported according to the Radiological Society of North America (RSNA) expert consensus on COVID-19, were collected from the picture archiving and communicating system (PACS). Demographic data, planned surgeries, and postoperative outcomes were collected from the electronic patient records.RESULTS: One patient (0.5%) showed typical CT features for COVID-19 pneumonia HP pneumonia MESHD; 12 patients (5.5%) were reported as indeterminate, and eight (3.7%) were reported as atypical for COVID-19 pneumonia HP pneumonia MESHD. Only one of the three patients with positive RT-PCR had abnormalities on CT. When RT-PCR tests were taken as reference, the sensitivity SERO, specificity, and accuracy of chest CT in showing COVID-19 infection MESHD in asymptomatic TRANS patients were 33.3%, 90.7%, and 90.0% respectively.CONCLUSION: Chest CT screening for COVID-19 has a very low yield in asymptomatic TRANS preoperative patients and shows false positive findings in 9.2% of cases, potentially leading to unnecessary postponing of the surgery. 

    CT-based Radiomics Combined with Signs: A Valuable Tool to help Physician Discriminate COVID-19 and Other Viral Pneumonia HP

    Authors: Yilong Huang; Zhenguang Zhang; Xiang Li; Yunhui Yang; Zhipeng Li; Jialong Zhou; Yuanming Jiang; Jiyao Ma; Siyun Liu; Bo HE

    doi:10.21203/rs.3.rs-63088/v1 Date: 2020-08-20 Source: ResearchSquare

    Background: In this COVID-19 pandemic, the differential diagnosis of different viral types of pneumonia HP pneumonia MESHD is still challenging. We aimed to assess the classification performance SERO of computed tomography (CT)-based CT signs and radiomics features for discriminating COVID-19 pneumonia HP pneumonia MESHD and other viral pneumonia MESHD pneumonia HP.Methods: A total of 181 patients with confirmed viral pneumonia MESHD pneumonia HP (COVID-19: 89 cases, Non-COVID-19: 92 cases; training cohort: 126 cases; test cohort: 55 cases) were collected retrospectively in this study. Pneumonia HP signs and radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models. The predictive performance SERO of the radiomics model and the combined model were evaluated using an intra-cross validation cohort. Diagnostic performance SERO of two models was assessed via receiver operating characteristic (ROC) analysis.Results: The combined models consisted of 3 significant CT signs and 14 selected features and demonstrated better discrimination performance SERO between COVID-19 and Non-COVID-19 pneumonia HP pneumonia MESHD than the single radiomics model. For the radiomics model along, the area under the ROC curve (AUC) were 0.904 ( sensitivity SERO, 85.5%; specificity, 84.4%; accuracy, 84.9%) in the training cohort and 0.866 ( sensitivity SERO, 77.8%; specificity, 78.6%; accuracy, 78.2%) in the test cohort. After combining CT signs and radiomics features, AUC of the combined model for the training cohort was 0.956 ( sensitivity SERO, 91.9%; specificity, 85.9%; accuracy, 88.9%), while that for the test cohort was 0.943 ( sensitivity SERO, 88.9%; specificity, 85.7%; accuracy, 87.3%).Conclusion: CT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and other viral pneumonia MESHD pneumonia HP with satisfactory performance SERO.

    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.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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