Corpus overview


MeSH Disease

Human Phenotype


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    MultiCheXNet: A Multi-Task Learning Deep Network For Pneumonia MESHD Pneumonia HP-like Diseases MESHD Diagnosis From X-ray Scans

    Authors: Abdullah Tarek Farag; Ahmed Raafat Abd El-Wahab; Mahmoud Nada; Mohamed Yasser Abd El-Hakeem; Omar Sayed Mahmoud; Reem Khaled Rashwan; Ahmad El Sallab

    id:2008.01973v1 Date: 2020-08-05 Source: arXiv

    We present MultiCheXNet, an end-to-end Multi-task learning model, that is able to take advantage of different X-rays data sets of Pneumonia MESHD Pneumonia HP-like diseases MESHD in one neural architecture, performing three tasks at the same time; diagnosis, segmentation and localization. The common encoder in our architecture can capture useful common features present in the different tasks. The common encoder has another advantage of efficient computations, which speeds up the inference time compared to separate models. The specialized decoders heads can then capture the task-specific features. We employ teacher forcing to address the issue of negative samples that hurt the segmentation and localization performance SERO. Finally,we employ transfer learning to fine tune the classifier on unseen pneumonia MESHD pneumonia HP-like diseases MESHD. The MTL architecture can be trained on joint or dis-joint labeled data sets. The training of the architecture follows a carefully designed protocol, that pre trains different sub-models on specialized datasets, before being integrated in the joint MTL model. Our experimental setup involves variety of data sets, where the baseline performance SERO of the 3 tasks is compared to the MTL architecture performance SERO. Moreover, we evaluate the transfer learning mode to COVID-19 data set,both from individual classifier model, and from MTL architecture classification head.

    Differential Diagnosis in Patients with HRCT Patterns Suspected for COVID-19 Pneumonia MESHD Pneumonia HP and RT-PCR Negative: A Multidisciplinary Approach for an Appropriate Management

    Authors: D'Onofrio Renato; D’Andreta Michela; Modolon Cecilia; Rimondi Maria Rita; Poggi Cristina; Di Scioscio Valerio; Trapani Filippo; Attard Luciano; Nava Stefano; Monteduro Francesco

    doi:10.21203/ Date: 2020-07-31 Source: ResearchSquare

    Since 11th March 2020, the World Health Organization has declared that coronavirus disease MESHD 2019 (COVID-19) caused by SARS-CoV-2 is a pandemic and a global public health emergency MESHD. One of the main problems related to the rapid spread of the pandemic was the low sensitivity SERO of the swabs that were used, resulting in high false negative rates, and the inability of HRCT to distinguish COVID-19 pneumonia MESHD pneumonia HP from other types of pneumonia MESHD pneumonia HP, resulting in diagnostic delays and increased infections MESHD in the hospital units. To deal with this problem, a multidisciplinary team has been created at the Sant'Orsola-Malpighi University Hospital in Bologna, with the aim of integrating the data of each patient framed as a "COVID suspect" and to get to a correct diagnosis. We analysed some exemplary cases of patients with negative swab but highly suggestive clinical and radiological characteristics for COVID-19 that, thanks to this multidisciplinary team, were then framed as other forms of pneumonia MESHD pneumonia HP and successfully treated.

    SARS-CoV-2 serology increases diagnostic accuracy in CT-suspected, PCR-negative COVID-19 patients during pandemic

    Authors: Jochen Schneider; Hrvoje Mijocevic; Kurt Ulm; Bernhardt Ulm; Simon Weidlich; Silvia Wuerstle; Kathrin Rothe; Matthias Treiber; Roman Iakoubov; Ulrich Mayr; Tobias Lahmer; Sebastian Rasch; Alexander Herner; Egon Burian; Fabian Lohöfer; Rickmer Braren; Marcus Makowski; Roland Schmid; Ulrike Protzer; Christoph Spinner; Fabian Geisler

    doi:10.21203/ Date: 2020-07-30 Source: ResearchSquare

    Background: In the absence of PCR detection of SARS-CoV-2 RNA, accurate diagnosis of COVID-19 is challenging. Low-dose computed tomography (CT) detects pulmonary infiltrates HP with high sensitivity SERO, but findings may be non-specific. This study assesses the diagnostic value of SARS-CoV-2 serology for patients with distinct CT features but negative PCR. Methods: IgM/IgG chemiluminescent immunoassay SERO was performed for 107 patients with confirmed (group A: PCR+; CT±) and 46 patients with suspected (group B: repetitive PCR-; CT+) COVID-19, admitted to a German university hospital during the pandemic’s first wave. A standardized, in-house CT classification of radiological signs of a viral pneumonia MESHD pneumonia HP was used to assess the probability of COVID-19. Results: Seroconversion rates (SR) determined on day 5, 10, 15, 20 and 25 after symptom onset TRANS (SO) were 8%, 25%, 65%, 76% and 91% for group A, and 0%, 10%, 19%, 37% and 46% for group B, respectively; (p<0.01). Compared to hospitalized patients with a non-complicated course, seroconversion tended to occur at lower frequency and delayed in patients on intensive care units. SR of patients with CT findings classified as high certainty for COVID-19 were 9%, 26%, 65%, 77% and 92% in group A, compared with 0%, 10%, 20%, 40% and 50% in group B (p<0.01). SARS-CoV-2 serology established a definite diagnosis in 12/46 group B patients. In 88% (8/9) of patients with negative serology >14 days after symptom onset TRANS (group B), clinico-radiological consensus reassessment revealed probable diagnoses other than COVID-19. Sensitivity SERO of SARS-CoV-2 serology was superior to PCR >17d after symptom onset TRANS. Conclusions: Approximately one-third of patients with distinct COVID-19 CT findings are tested negative for SARS-CoV-2 RNA by PCR rendering correct diagnosis difficult. Implementation of SARS-CoV-2 serology testing alongside current CT/PCR-based diagnostic algorithms improves discrimination between COVID-19-related and non-related pulmonary infiltrates HP in PCR negative patients. However, sensitivity SERO of SARS-CoV-2 serology strongly depends on the time of testing and becomes superior to PCR after the 2 nd week following symptom onset TRANS.

    PDCOVIDNet: A Parallel-Dilated Convolutional Neural Network Architecture for Detecting COVID-19 from Chest X-Ray Images

    Authors: Nihad Karim Chowdhury; Md. Muhtadir Rahman; Muhammad Ashad Kabir

    id:2007.14777v1 Date: 2020-07-29 Source: arXiv

    The COVID-19 pandemic continues to severely undermine the prosperity of the global health system. To combat this pandemic, effective screening techniques for infected patients are indispensable. There is no doubt that the use of chest X-ray images for radiological assessment is one of the essential screening techniques. Some of the early studies revealed that the patient's chest X-ray images showed abnormalities, which is natural for patients infected with COVID-19. In this paper, we proposed a parallel-dilated convolutional neural network (CNN) based COVID-19 detection system from chest x-ray images, named as Parallel-Dilated COVIDNet (PDCOVIDNet). First, the publicly available chest X-ray collection fully preloaded and enhanced, and then classified by the proposed method. Differing convolution dilation rate in a parallel form demonstrates the proof-of-principle for using PDCOVIDNet to extract radiological features for COVID-19 detection. Accordingly, we have assisted our method with two visualization methods, which are specifically designed to increase understanding of the key components associated with COVID-19 infection MESHD. Both visualization methods compute gradients for a given image category related to feature maps of the last convolutional layer to create a class-discriminative region. In our experiment, we used a total of 2,905 chest X-ray images, comprising three cases (such as COVID-19, normal, and viral pneumonia MESHD pneumonia HP), and empirical evaluations revealed that the proposed method extracted more significant features expeditiously related to the suspected disease MESHD. The experimental results demonstrate that our proposed method significantly improves performance SERO metrics: accuracy, precision, recall SERO, and F1 scores reach 96.58%, 96.58%, 96.59%, and 96.58%, respectively, which is comparable or enhanced compared with the state-of-the-art methods.

    CovMUNET: A Multiple Loss Approach towards Detection of COVID-19 from Chest X-ray

    Authors: A. Q. M. Sazzad Sayyed; Dipayan Saha; Abdul Rakib Hossain

    id:2007.14318v1 Date: 2020-07-28 Source: arXiv

    The recent outbreak of COVID-19 has halted the whole world, bringing a devastating effect on public health, global economy, and educational systems. As the vaccine of the virus is still not available, the most effective way to combat the virus is testing and social distancing. Among all other detection techniques, the Chest X-ray (CXR) based method can be a good solution for its simplicity, rapidity, cost, efficiency, and accessibility. In this paper, we propose CovMUNET, which is a multiple loss deep neural network approach to detect COVID-19 cases from CXR images. Extensive experiments are performed to ensure the robustness of the proposed algorithm and the performance SERO is evaluated in terms of precision, recall SERO, accuracy, and F1-score. The proposed method outperforms the state-of-the-art approaches with an accuracy of 96.97% for 3-class classification (COVID-19 vs normal vs pneumonia MESHD pneumonia HP) and 99.41% for 2-class classification (COVID vs non-COVID). The proposed neural architecture also successfully detects the abnormality in CXR images.

    Point-of-care ultrasound for COVID-19 pneumonia MESHD pneumonia HP patients in the ICU

    Authors: zouheir bitar; Mohammed Shamsah; Omar Bamasood; Ossama Maadrani; Huda Al foudri

    doi:10.21203/ Date: 2020-07-26 Source: ResearchSquare

    BackgroundPoint-of-care ultrasound (POCUS) has a major role in the management of patients with acute hypoxic respiratory and circulatory failure and guides hemodynamic management. There is scarce literature on POCUS assessment characteristics in COVID-19 pneumonia MESHD pneumonia HP with hypoxic respiratory failure HP.MethodsThe study is an observational, prospective, single‐center study conducted in the intensive care unit of Adan General Hospital from May 1st, 2020, to June 25, 2020. The study included adults TRANS suspected to have COVID-19 transferred to the intensive care unit (ICU) with fever MESHD fever HP or suspected respiratory infection MESHD. Patients were transferred to the ICU directly from the ED or general medical wards after reverse transcriptase-polymerase chain reaction (RT-PCR) testing. A certified intensivist in critical care ultrasound who was blinded to the RT-PCR results, if available at the time of examination, performed the lung ultrasound and echocardiology within 12 hours of the patient’s admission to the ICU. We calculated the E/e’, E/A ratio, left ventricular ejection fraction EF, IVC diameter, RV size and systolic function. We performed ultrasound in 12 chest areas.ResultsOf 92 patients with suspected COVID-19 pneumonia MESHD pneumonia HP, 77 (84%) cases were confirmed TRANS. The median age TRANS of the patients was 53 (82-36) years, and 71 (77%) were men.In the group of patients with confirmed COVID-19 pneumonia MESHD pneumonia HP, echocardiographic findings showed normal E/e’, deceleration time (DT), and transmittal E/A ratio in comparison to the non-COVID19 patients (P .001 for both). The IVC diameter was <2 cm with > 50% collapsibility in 62 (81%) patients with COVID-19 pneumonia MESHD pneumonia HP; a diameter of > 2 cm and < 50% collapsibility in all patients, with a P value of 0.001, was detected among those with non-COVID-19 pneumonia MESHD pneumonia HP. There were 3 cases of myocarditis MESHD myocarditis HP with poor EF (5.5%), severe RV dysfunction was seen in 9 cases (11.6%), and 3 cases showed RV thrombus.Chest US revealed four signs suggestive of COVID-19 pneumonia MESHD pneumonia HP in 77 patients (98.6%) ( sensitivity SERO 96.9%, CI 85%‐99.5%) when compared with RT-PCR results.ConclusionPOCUS plays an important role in bedside diagnosis, hemodynamic assessment and management of patients with acute hypoxic respiratory and circulatory failure in patients with COVID-19 pneumonia MESHD pneumonia HP.

    Ocular findings and retinal involvement in COVID-19 pneumonia MESHD pneumonia HP patients: A cross-sectional study in an Italian referral centre

    Authors: Maria Pia Pirraglia; Giancarlo Ceccarelli; Alberto Cerini; Giacomo Visioli; Gabriella d'Ettorre; Claudio Maria Mastroianni; Francesco Pugliese; Alessandro Lambiase; Magda Gharbiya

    doi:10.21203/ Date: 2020-07-23 Source: ResearchSquare

    Background: changes in immune and coagulation systems and possible viral spread through blood SERO-brain barrier have been described in SARS-CoV-2 infection MESHD. In this study, we evaluate the possible retinal involvement and ocular findings in severe COVID-19 pneumonia MESHD pneumonia HP patients.  Methods: a cross sectional study was conducted on 46 patients affected by severe COVID-19 who were hospitalized in one Intensive Care Unit (ICU) and in two Infectious Diseases MESHD wards, including a bedside eye screening, corneal sensitivity SERO assessment and retinography. Results: a total of 43 SARS-CoV-2 positive pneumonia MESHD pneumonia HP patients affected with COVID-19 pneumonia MESHD pneumonia HP were included, 25 males TRANS and 18 females TRANS, with a median age TRANS of 70 [IQR 59-78]. Except for one patient with unilateral posterior chorioretinitis MESHD chorioretinitis HP of opportunistic origin, of whom aqueous tap was negative for SARS-CoV-2, no further retinal manifestation related to COVID-19 infection MESHD was found in our cohort. We found 3 patients (7%) with bilateral conjunctivitis MESHD conjunctivitis HP in whom PCR analysis on conjunctival swab provided negative results for SARS-CoV-2. No alterations of corneal sensitivity SERO were found.Conclusion: we demonstrated the absence of retinal involvement in SARS-CoV-2 pneumonia MESHD pneumonia HP patients. Ophthalmologic evaluation in COVID-19, particularly in patients hospitalized in an ICU setting, may be useful to reveal systemic co- infections by opportunistic MESHD infections by opportunistic HP pathogens. 

    Integrative Analysis for COVID-19 Patient Outcome Prediction

    Authors: Hanqing Chao; Xi Fang; Jiajin Zhang; Fatemeh Homayounieh; Chiara D. Arru; Subba R. Digumarthy; Rosa Babaei; Hadi K. Mobin; Iman Mohseni; Luca Saba; Alessandro Carriero; Zeno Falaschi; Alessio Pasche; Ge Wang; Mannudeep K. Kalra; Pingkun Yan

    id:2007.10416v1 Date: 2020-07-20 Source: arXiv

    While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia MESHD pneumonia HP, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression MESHD with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia MESHD pneumonia HP. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance SERO of prediction to achieve AUC up to 0.884 and sensitivity SERO as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases MESHD including but not limited to community acquired pneumonia MESHD pneumonia HP.

    Clinical Severity and CT Features of the COVID-19 Pneumonia MESHD Pneumonia HP: Focus on CT Score and Laboratory Parameters

    Authors: Jianghui Duan; Kunsong Su; Hongliang Sun; Yanyan Xu; Liangying Liu

    doi:10.21203/ Date: 2020-07-18 Source: ResearchSquare

    Background: Although CT characteristics of Coronavirus Disease MESHD 2019 (COVID-19) pneumonia MESHD pneumonia HP between patients with mild and severe forms of the disease MESHD have already been reported in the literature, there was little attention to the correlation of imaging features and laboratory testing. We aimed to compare the laboratory and chest CT imaging features in patients with COVID-19 pneumonia MESHD pneumonia HP between non-severe cases and severe cases, and to analyze the correlation of CT score and laboratory testing.Methods: This study consecutively included 54 patients with COVID-19 pneumonia MESHD pneumonia HP (26 males TRANS and 28 females TRANS, 26 to 92 years of age TRANS, 43 cases with non-severe and 11 cases with severe group). Clinical, laboratory and image data were collected between two subgroups. A CT score system was used to evaluate the extent of disease MESHD. Correlation between the CT score and laboratory data were estimated. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance SERO of CT score and laboratory tests.Results: Compared with non-severe patients, severe patients had showed increased white blood SERO cell count, neutrophil count, neutrophil percentage, the neutrophil-to-lymphocyte ratio (NLR) and decreased lymphocyte percentage (all p < 0.05). Architectural distortion, pleural effusion MESHD pleural effusion HP, air bronchogram and consolidation-dominant pattern were more common in the severe group (all p < 0.05). CT score of the severe group was higher than the non-severe group (p < 0.001). For distribution characters of the lesions, diffuse pattern in the transverse distribution was more often seen in the severe group (p < 0.001). CT score was positively correlated with the white blood SERO cell counts, neutrophil counts, the percent of neutrophil, NLR, alanine aminotransferase, lactate dehydrogenase and C-reactive protein, and was inversely related to the lymphocyte, the percent of lymphocyte. ROC analysis showed that when the optimal threshold of CT score was 13, the area under the curve was the largest, which was 0.855, and the sensitivity SERO and specificity were 100% and 60% respectively for the diagnosis of the severe patients.Conclusion: CT score showed significant correlations with laboratory inflammatory markers, suggesting that chest CT and laboratory examination maybe provide a better reference for clinicians to judge the severity of diseases MESHD.

    COVID-19 Detection on Chest X-Ray and CT Scan Images Using Multi-image Augmented Deep Learning Model

    Authors: Kiran Purohit; Abhishek Kesarwani; Dakshina Ranjan Kisku; Mamata Dalui

    doi:10.1101/2020.07.15.205567 Date: 2020-07-17 Source: bioRxiv

    COVID-19 is posed as very infectious and deadly pneumonia MESHD pneumonia HP type disease MESHD until recent time. Novel coronavirus or SARS-COV-2 strain is responsible for COVID-19 and it has already shown the deadly nature of respiratory disease MESHD by threatening the health of millions of lives across the globe. Clinical study reveals that a COVID-19 infected person may experience dry cough MESHD cough HP, muscle pain MESHD pain HP, headache MESHD headache HP, fever MESHD fever HP, sore throat and mild to moderate respiratory illness. At the same time, it affects the lungs badly with virus infection MESHD. So, the lung can be a prominent internal organ to diagnose the gravity of COVID-19 infection MESHD using X-Ray and CT scan images of chest. Despite having lengthy testing time, RT-PCR is a proven testing methodology to detect coronavirus infection MESHD. Sometimes, it might give more false positive and false negative results than the desired rates. Therefore, to assist the traditional RT-PCR methodology for accurate clinical diagnosis, COVID-19 screening can be adopted with X-Ray and CT scan images of lung of an individual. This image based diagnosis will bring radical change in detecting coronavirus infection MESHD in human body with ease and having zero or near to zero false positives and false negatives rates. This paper reports a convolutional neural network (CNN) based multi-image augmentation technique for detecting COVID-19 in chest X-Ray and chest CT scan images of coronavirus suspected individuals. Multi-image augmentation makes use of discontinuity information obtained in the filtered images for increasing the number of effective examples for training the CNN model. With this approach, the proposed model exhibits higher classification accuracy around 95.38% and 98.97% for CT scan and X-Ray images respectively. CT scan images with multi-image augmentation achieves sensitivity SERO of 94.78% and specificity of 95.98%, whereas X-Ray images with multi-image augmentation achieves sensitivity SERO of 99.07% and specificity of 98.88%. Evaluation has been done on publicly available databases containing both chest X-Ray and CT scan images and the experimental results are also compared with ResNet-50 and VGG-16 models.

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MeSH Disease
Human Phenotype

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