Query: chest x-ray  semi viral pneumonia

title: Differentiation of Bacterial and Viral Pneumonia in Children by Hematological Investigations
abstract: To differentiate bacterial and viral community acquired pneumonias using WBC count (TC) differential count (DC),erythrocyte sedimentation rate(ESR) Design: Descriptive Study Setting: Semi Urban tertiary care hospital  100 cases of children aged between 1month to 12 years who were admitted to paediatric wards with tachypnoea as per WHO criteria and chest in drawing in Rajah Muthiah Medical college, Chidambaram were included in the study. Detailed history, physical examinations were recorded in a proforma. Investigations included WBC count, DC, ESR, Chest X ray. Children who had radiologically confirmed pneumonia (86 cases) were divided in to bacterial and viral pneumonia based on their radiological findings. These results were correlated with laboratory findings and results were given.  In the present study, radiological findings were present in 86% of cases. Bacterial pneumonia was detected in 73% viral pneumonia in 13% Among bacterial pneumonia, bronchopneumonia was seen in 34% cases, consolidation was seen in 26% alveolar infiltrate in 6% and complications of pneumonia in 7% cases. In the present study, among bacterial pneumonias defined radiologically,63% had elevated WBC count ,79% had neutrophilia, 76.7% had elevated ESR. There was no correlation of laboratory findings with bacterial and viral pneumonia defined radiologically (low sensitivity and specificity)  Routine hematological investigations like WBC count, DC, ESR aid very little in differentiating bacterial and viral pneumonias. Chest X ray is valuable aid in the diagnosis of pneumonia in children. Follow up chest roentgenogram is vital for evaluating the response to treatment in pneumonia.

title: Human Rhinovirus is the Most Commonly Identified Respiratory Virus in Children 1 Hospitalized with Pneumonia from Mozambique 2 3
abstract: To describe the prevalence of human rhinovirus (RV) species in children 27 hospitalized with pneumonia in Manhica Mozambique and the associations between RV 28 species and demographic, clinical, and laboratory features.  Nasopharyngeal aspirates were collected from children 0 10 years of age 30 (n=277) presenting to the Manhica District Hospital in Mozambique with clinical pneumonia.  Blood samples were collected for HIV and malaria testing, blood culture and full blood 32 counts, and a chest X ray was performed. A panel of common respiratory viruses was 33 investigated using two independent multiplex RT PCR assays with primers specific for each 34 virus and viral type. RV species and genotypes were identified by semi nested PCR assays, 35 sequencing and phylogenetic tree analyses.  At least one respiratory virus was identified in 206 (74.4% children hospitalized 37 with clinical pneumonia. RV was the most common virus identified in both HIV infected 38 (17/38, 44.7% and HIV uninfected (74/237, 31.2% p=0.100) children. RV A was the most 39 common RV species identified (47/275, 17.0% followed by RV C (35/275, 12.6% and RV40 B (8/275, 2.9% Clinical presentation of the different RV species was similar and 41 overlapping, with no particular species being associated with specific clinical features.  RV A and RV C were the most common respiratory viruses identified in 43 children hospitalised with clinical pneumonia in Manhica.  Acute lower respiratory infections (ALRI) such as pneumonia account for an estimated 1.3 48 million deaths each year in children under 5 years of age, 43% of which occur in sub Saharan 49 Africa.(1) Management and prevention efforts against pneumonia in developing countries 50 have traditionally focused on bacterial pathogens. The introduction of effective conjugate 51 vaccines globally has led to decreasing trends in bacterial pneumonia and a subsequent 52 increased interest in the role of virus associated ALRI. Recent advances in molecular 53 diagnostics such as PCR have also led to the discovery of new viruses and viral species, 54 highlighting the prominence of viruses in respiratory disease. Respiratory viruses are widely 55 acknowledged to be the most common cause of both upper and lower respiratory tract 56 infections in the developed world.(2) However, advanced diagnostics are largely limited in 57 the developing world and studies including a comprehensive range of viral pathogens are 58 scarce in many African countries. The majority of respiratory viral aetiological studies in 59 Africa have relied on traditional cell culture and serological methods with PCR data only 60 becoming available in recent years. Data beyond respiratory syncytial virus (RSV) are still 61 scarce and aetiological studies of ALRI in African children published as recently as 2013 did 62 not screen for common respiratory viruses such as RV, coronavirus and bocavirus(3) or were 63 unable to distinguish RV from enterovirus.(4) 64 65 RV is the most common cause of childhood respiratory infections worldwide and responsible 66 for almost two thirds of cases of the common cold(5) as well as lower respiratory tract 67 infections, including pneumonia and bronchiolitis. The identification of RV C as the third RV 68 species, first reported in 2006, led to several investigations on the prevalence of RV species, 69 conducted predominantly in developed countries. The majority of these studies on children 70 hospitalized with ALRI or asthma found that RV C was the most prevalent RV species and 71 was often associated with more severe illness.(6 13) While there have been previous reports 72 on the overall prevalence of RV in children with acute respiratory infections or pneumonia in 73 Mozambique, none have specifically investigated RV species.(14, 15) In the two 74 Mozambican studies of respiratory viral prevalence, RV was the most commonly identified 75 virus, identified in 26% and 24% of children with acute respiratory infections and 76 pneumonia, respectively. Only three other studies have investigated the prevalence of RV 77 species in African children.(16 18) While these studies confirmed the importance of RV in 78 African children, they were inconclusive with respect to the role of RV species in ALRI in 79 African children. 80 81 Much of our understanding on the viral aetiology of childhood pneumonia in Africa is based 82 on studies conducted prior to the HIV epidemic that has engulfed many African countries. 83 Evidence suggests that HIV infection is now driving both the frequency and outcome of 84 pneumonia and pneumonia is the leading cause of morbidity and mortality in HIV infected 85 children.(19) A recent study from South Africa reported that a respiratory virus was identified 86 in the majority of both HIV infected and HIV uninfected children, with RV being the most 87 frequently identified virus.(20) No studies have investigated RV species among HIV infected 88 children. 89 90 Given the limitations of routine microbiology facilities in the majority of African countries, 91 most clinicians rely on examination of clinical features to determine the probable aetiology of 92 ALRI in children. Clinical features, which are often indistinguishable for different respiratory 93 viruses, (21, 22) have not been comprehensively investigated in childhood ALRI in Africa. 94 A few studies from Africa and the Middle East found that RV C was associated with 95 wheezing.(12, 18) The clinical relevance of viral co infections is also not well established 96 and there is conflicting evidence regarding the association between multiple viral 97 identifications and disease severity. Some studies have reported an association between viral 98 co infections and disease severity (23 29) while others have reported no differences in 99 disease severity between single and multiple viral infections.(30, 31) 100 101 The aim of this study was to describe the prevalence of respiratory viruses, in particular RV 102 and RV species, and the association of RV species with HIV status, clinical features and 103 seasonality in children with clinical pneumonia from Manhica, Mozambique. 104 105 Materials and Methods 106 Study  This study was conducted by the Manhica Health Research Centre (Centro de Investigacao 108 em Saude da Manhica, CISM) at the Manhica District Hospital (MDH) a public hospital in 109 Southern Mozambique. The Manhica district in Southern Mozambique has an estimated 110 population of 143,000. The area has a subtropical climate with two distinct seasons; a warm 111 and rainy season between November and April and cool and dry season during the rest of the 112 year. The HIV prevalence among newborns has been estimated between 2.9 and 8%(15) A 113 demographic surveillance system (DSS) including 500km 2 surrounding the area, developed 114 by CISM, has been running since 1996, and covers a population of around 92,000 inhabitants. 115 Each individual living within the DSS area is issued a unique permanent identification 116 number and information on vital events are collected during routine household visits.(32) 117 Further characteristics of the DSS and study area are described elsewhere.(32) 118 119 Children were recruited as part of a larger project aiming to investigate the underlying 120 aetiology of children with respiratory symptoms. Between September 2010 and April 2013, 121 277 children 0 10 years of age presenting to the Manhica District Hospital with fever (or a 122 history of fever >37.5oC axillary temperature) in the preceding 24 hours) and clinical 123 pneumonia, according to the WHO definition (increased respiratory rate and cough and/or 124 difficulty in breathing) and sick enough to warrant hospital admission, were recruited into 125 the study. Increased respiratory rate was defined according to Integrated Management of 126 Childhood (IMCI) guidelines: 60 breaths per minute in children 2 months, 50 in 2 12 127 months, 40 in 1 5 years, and 30 in 5 10 years) Exclusion criteria included prior 128 enrolment in this study, use of antibiotics or antimalarial drugs during the preceding two 129 weeks, history of cough for more than two weeks duration, active tuberculosis or history of 130 direct contact with a documented tuberculosis case, and children with an oxyhaemoglobin 131 saturation of less than 85% on examination on admission, as a proxy for possible 132 Pneumocystis jirovecii infection. 133 134 Clinical and questionnaire data and samples were obtained from the enrolled cases on the day 135 of recruitment. A NPA was collected from each child by a trained study health assistant. HIV 136 testing was routinely conducted, and all patients underwent chest X ray and extensive clinical 137 and laboratorial screening, including malaria testing, blood culture and full blood counts. 138 139 Written informed consent was obtained from parents or guardians prior to participation and 140 the study was approved by the University of Western Australia Human Research Ethics 141 Committee, the Ethics Committee of the Hospital Clinic (Barcelona, Spain) and the Comite 142 Nacional de Bioetica para a Saude (Maputo, Mozambique) prior to commencement. 143 144 Laboratory methods 145 Virus detection 146 NPAs were stored at 80oC in Manhica, Mozambique until shipped to the study laboratories, 147 on dry ice, for processing. Identification of common respiratory viruses (adenovirus, RSV, 148 bocavirus, coronavirus, parainfluenza viruses, influenza viruses and metapneumovirus) was 149 carried out using two independent multiplex RT PCR assays with primers specific for each 150 virus and viral type. RV identification and genotyping was based on a published molecular 151 method to determine RV genotypes and to differentiate closely related enteroviruses from 152 RV.

title: Viral Pneumonia Screening on Chest X ray Images Using Confidence Aware Anomaly Detection
abstract: Cluster of viral pneumonia occurrences during a short period of time may be a harbinger of an outbreak or pandemic, like SARS, MERS, and recent COVID 19. Rapid and accurate detection of viral pneumonia using chest X ray can be significantly useful in large scale screening and epidemic prevention, particularly when other chest imaging modalities are less available. Viral pneumonia often have diverse causes and exhibit notably different visual appearances on X ray images. The evolution of viruses and the emergence of novel mutated viruses further result in substantial dataset shift, which greatly limits the performance of classification approaches. In this paper, we formulate the task of differentiating viral pneumonia from non viral pneumonia and healthy controls into an one class classification based anomaly detection problem, and thus propose the confidence aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module. If the anomaly score produced by the anomaly detection module is large enough or the confidence score estimated by the confidence prediction module is small enough, we accept the input as an anomaly case (i.e. viral pneumonia) The major advantage of our approach over binary classification is that we avoid modeling individual viral pneumonia classes explicitly and treat all known viral pneumonia cases as anomalies to reinforce the one class model. The proposed model outperforms binary classification models on the clinical X VIRAL dataset that contains 5,977 viral pneumonia (no COVID 19) cases, 18,619 non viral pneumonia cases, and 18,774 healthy controls.

title: CovXNet: A multi dilation convolutional neural network for automatic COVID 19 and other pneumonia detection from chest X ray images with transferable multi receptive feature optimization
abstract: With the recent outbreak of COVID 19, fast diagnostic testing has become one of the major challenges due to the critical shortage of test kit. Pneumonia, a major effect of COVID 19, needs to be urgently diagnosed along with its underlying reasons. In this paper, deep learning aided automated COVID 19 and other pneumonia detection schemes are proposed utilizing a small amount of COVID 19 chest X rays. A deep convolutional neural network (CNN) based architecture, named as CovXNet, is proposed that utilizes depthwise convolution with varying dilation rates for efficiently extracting diversified features from chest X rays. Since the chest X ray images corresponding to COVID 19 caused pneumonia and other traditional pneumonias have significant similarities, at first, a large number of chest X rays corresponding to normal and (viral/bacterial) pneumonia patients are used to train the proposed CovXNet. Learning of this initial training phase is transferred with some additional fine tuning layers that are further trained with a smaller number of chest X rays corresponding to COVID 19 and other pneumonia patients. In the proposed method, different forms of CovXNets are designed and trained with X ray images of various resolutions and for further optimization of their predictions, a stacking algorithm is employed. Finally, a gradient based discriminative localization is integrated to distinguish the abnormal regions of X ray images referring to different types of pneumonia. Extensive experimentations using two different datasets provide very satisfactory detection performance with accuracy of 97.4% for COVID/Normal, 96.9% for COVID/Viral pneumonia, 94.7% for COVID/Bacterial pneumonia, and 90.2% for multiclass COVID/normal/Viral/Bacterial pneumonias. Hence, the proposed schemes can serve as an efficient tool in the current state of COVID 19 pandemic. All the architectures are made publicly available at: https:/github.com/Perceptron21/CovXNet.

title: WITHDRAWN: Deep Learning Classification For Diagnosis COVID 19 Between Bacterial Pneumonia and Viral Pneumonia in Chest X Ray Images

title: Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X ray
abstract: Pneumonia is a life threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life endangering if not acted upon at the right time and thus the early diagnosis of pneumonia is vital. The paper aims to automatically detect bacterial and viral pneumonia using digital x ray images. It provides a detailed report on advances in accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre trained deep Convolutional Neural Network (CNN) AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. A total of 5247 chest X ray images consisting of bacterial, viral, and normal chest x rays images were preprocessed and trained for the transfer learning based classification task. In this study, the authors have reported three schemes of classifications: normal vs. pneumonia, bacterial vs. viral pneumonia, and normal, bacterial, and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial, and viral pneumonia were 98% 95% and 93.3% respectively. This is the highest accuracy, in any scheme, of the accuracies reported in the literature. Therefore, the proposed study can be useful in more quickly diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.

title: Screening of Viral Pneumonia and COVID 19 in Chest X ray using Classical Machine Learning
abstract: Governments, civil society, health professionals, and scientists have been facing a relentless fight against the pandemic of the COVID 19 disease; however, there are already about 150 million people infected worldwide and more than 3 million lives claimed, and numbers keep rising. One of the ways to combat this disease is the effective screening of infected patients. However, COVID 19 provides a similar pattern with diseases, such as pneumonia, and can misguide even very well trained physicians. In this sense, a chest X ray (CXR) is an effective alternative due to its low cost, accessibility, and quick response. Thus, inspired by research on the use of CXR for the diagnosis of COVID 19 pneumonia, we investigate classical machine learning methods to assist in this task. The main goal of this work is to present a robust, lightweight, and fast technique for the automatic detection of COVID 19 from CXR images. We extracted radiomic features from CXR images and trained classical machine learning models for two different classification schemes: i) COVID 19 pneumonia vs. Normal ii) COVID 19 vs. Normal vs. Viral pneumonia. Several evaluation metrics were used and comparison with many studies is presented. Our experimental results are equivalent to the state of the art for both classification schemes. The solution's high performance makes it a viable option as a computer aided diagnostic tool, which can represent a significant gain in the speed and accuracy of the COVID 19 diagnosis.

title: Hybrid ensemble model for differential diagnosis between COVID 19 and common viral pneumonia by chest X ray radiograph
abstract: Chest X ray radiography (CXR) has been widely considered as an accessible, feasible, and convenient method to evaluate suspected patients' lung involvement during the COVID 19 pandemic. However, with the escalating number of suspected cases, traditional diagnosis via CXR fails to deliver results within a short period of time. Therefore, it is crucial to employ artificial intelligence (AI) to enhance CXRs for obtaining quick and accurate diagnoses. Previous studies have reported the feasibility of utilizing deep learning methods to screen for COVID 19 using CXR and CT results. However, these models only use a single deep learning network for chest radiograph detection; the accuracy of this approach required further improvement.  In this study, we propose a three step hybrid ensemble model, including a feature extractor, a feature selector, and a classifier. First, a pre trained AlexNet with an improved structure extracts the original image features. Then, the ReliefF algorithm is adopted to sort the extracted features, and a trial and error approach is used to select the n most important features to reduce the feature dimension. Finally, an SVM classifier provides classification results based on the n selected features.  Compared to five existing models (InceptionV3: 97.916 0.408% SqueezeNet: 97.189 0.526% VGG19: 96.520 1.220% ResNet50: 97.476 0.513% ResNet101: 98.241 0.209% the proposed model demonstrated the best performance in terms of overall accuracy rate (98.642 0.398% Additionally, compared to the existing models, the proposed model demonstrates a considerable improvement in classification time efficiency (SqueezeNet: 6.602 ; InceptionV3: 12.376 0.002s; ResNet50: 10.952 0.001s; ResNet101: 18.040 0.002s; VGG19: 16.632 0.002s; proposed model: 5.917 )  The model proposed in this article is practical and effective, and can provide high precision COVID 19 CXR detection. We demonstrated its suitability to aid medical professionals in distinguishing normal CXRs, viral pneumonia CXRs and COVID 19 CXRs efficiently on small sample sizes.

title: Training deep learning algorithms with weakly labeled pneumonia chest X ray data for COVID 19 detection
abstract: The novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV 2) has caused a pandemic resulting in over 2.7 million infected individuals and over 190,000 deaths and growing. Respiratory disorders in COVID 19 caused by the virus commonly present as viral pneumonia like opacities in chest X ray images which are used as an adjunct to the reverse transcription polymerase chain reaction test for confirmation and evaluating disease progression. The surge places high demand on medical services including radiology expertise. However, there is a dearth of sufficient training data for developing image based automated decision support tools to alleviate radiological burden. We address this insufficiency by expanding training data distribution through use of weakly labeled images pooled from publicly available CXR collections showing pneumonia related opacities. We use the images in a stage wise, strategic approach and train convolutional neural network based algorithms to detect COVID 19 infections in CXRs. It is observed that weakly labeled data augmentation improves performance with the baseline test data compared to non augmented training by expanding the learned feature space to encompass variability in the unseen test distribution to enhance inter class discrimination, reduce intra class similarity and generalization error. Augmentation with COVID 19 CXRs from individual collections significantly improves performance compared to baseline non augmented training and weakly labeled augmentation toward detecting COVID 19 like viral pneumonia in the publicly available COVID 19 CXR collections. This underscores the fact that COVID 19 CXRs have a distinct pattern and hence distribution, unlike non COVID 19 viral pneumonia and other infectious agents.

title: Which role for chest x ray score in predicting the outcome in COVID 19 pneumonia?
abstract: We aim to demonstrate that a chest X ray (CXR) scoring system for COVID 19 patients correlates with patient outcome and has a prognostic value. This retrospective study included CXRs of COVID 19 patients that reported the Brixia score, a semi quantitative scoring system rating lung involvement from 0 to 18. The highest (H) and lowest (L) values were registered along with scores on admission (A) and end of hospitalization (E) The Brixia score was correlated with the outcome (death or discharge) A total of 953 patients met inclusion criteria. In total, 677/953 were discharged and 276/953 died during hospitalization. A total of 524/953 had one CXR and 429/953 had more than one CXR. H score was significantly higher in deceased (median, 12; IQR 9 14) compared to that in discharged patients (median, 8; IQR 5 11) (p 0.0001) In 429/953 patients with multiple CXR, A score, L score, and E score were higher in deceased than in discharged patients (A score 9 vs 8; p 0.039; L score 7 vs 5; p 0.0003; E score 12 vs 7; p 0.0001) In the entire cohort, logistic regression showed a significant predictive value for age (p 0.0001, OR 1.13) H score (p 0.0001, OR 1.25) and gender (p 0.01, male OR 1.67) AUC was 0.863. In patients with 2 CXR, A L and E scores correlated significantly with the outcome. Cox proportional hazards regression indicated age (p 0.0001, HR 4.17) H score 9, HR 0.36, p 0.0012) and worsening of H score vs A score 3 (HR 1.57, p 0.0227) as associated with worse outcome. The Brixia score correlates strongly with disease severity and outcome; it may support the clinical decision making, particularly in patients with moderate to severe signs and symptoms. The Brixia score should be incorporated in a prognostic model, which would be desirable, particularly in resource constraint scenarios. To demonstrate the importance of the Brixia score in assessing and monitoring COVID 19 lung involvement. The Brixia score strongly correlates with patient outcome and can be easily implemented in the routine reporting of CXR.