Pediatric chest x-ray in covid-19 infection

Evaluation of Lung Ultrasonography Findings of Children With Late Respiratory System Symptoms Due to COVID-19 Infection

Owing to coronavirus disease 2019 (COVID-19), lung damage is seen as an important problem in patients after recovery. In this study, evaluation of respiratory symptoms and lung ultrasonography (LUS) findings of those who have had symptomatic and asymptomatic COVID-19 disease in children was aimed. A total of 81 patients with positive and 18 healthy children with negative COVID-19 antibodies were included to the study. The most common late presentation symptoms were cough (85.2%), shortness of breath (77.8%), and chest pain (60.5%). In LUS, 2 or less B lines, 3 or more B lines, and Z line were seen in 66.7%, 33.3%, and 9.9% of patients, respectively. There was no significant difference between control and patients in terms of these parameters (P > .05). Pleural effusion was detected in 2 patients in the late period. Respiratory system findings may develop in the late period in patients infected with COVID-19. Therefore, patients should be followed closely.

Radiographic patterns and severity scoring of COVID-19 pneumonia in children: a retrospective study

BACKGROUND

Chest radiography (CXR) is an adjunct tool in treatment planning and monitoring of the disease course of COVID-19 pneumonia. The purpose of the study was to describe the radiographic patterns and severity scores of abnormal CXR findings in children diagnosed with COVID-19 pneumonia.

METHODS

This retrospective study included children with confirmed COVID-19 by reverse transcriptase-polymerase chain reaction test who underwent CXR at the arrival. The CXR findings were reviewed, and modified radiographic scoring was assessed.

RESULTS

The number of abnormal CXR findings was 106 of 976 (10.9%). Ground-glass opacity (GGO) was commonly found in children aged > 9 years (19/26, 73.1%), whereas peribronchial thickening was predominantly found in children aged < 5 years (25/54, 46.3%). Overall, the most common radiographic finding was peribronchial thickening (54/106, 51%). The lower lung zone (56/106, 52.8%) was the most common affected area, and there was neither peripheral nor perihilar predominance (84/106, 79.2%). Regarding the severity of COVID-19 pneumonia based on abnormal CXR findings, 81 of 106 cases (76.4%) had mild lung abnormalities. Moderate and severe lung abnormalities were found in 21 (19.8%) and 4 (3.8%) cases, respectively. While there were no significant differences in the radiographic severity scores among the various pediatric age groups, there were significant disparities in severity scores in the initial CXR and medical treatments.

CONCLUSIONS

This study clarified the age distribution of radiographic features across the pediatric population. GGO was commonly found in children aged > 9 years, whereas peribronchial thickening was predominant in children aged < 5 years. The lower lung zone was the most common affected area, and the high severity lung scores required more medical treatments and oxygen support.

Biases associated with database structure for COVID-19 detection in X-ray images

Several artificial intelligence algorithms have been developed for COVID-19-related topics. One that has been common is the COVID-19 diagnosis using chest X-rays, where the eagerness to obtain early results has triggered the construction of a series of datasets where bias management has not been thorough from the point of view of patient information, capture conditions, class imbalance, and careless mixtures of multiple datasets. This paper analyses 19 datasets of COVID-19 chest X-ray images, identifying potential biases. Moreover, computational experiments were conducted using one of the most popular datasets in this domain, which obtains a 96.19% of classification accuracy on the complete dataset. Nevertheless, when evaluated with the ethical tool Aequitas, it fails on all the metrics. Ethical tools enhanced with some distribution and image quality considerations are the keys to developing or choosing a dataset with fewer bias issues. We aim to provide broad research on dataset problems, tools, and suggestions for future dataset developments and COVID-19 applications using chest X-ray images.

RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics

Deep learning (DL) algorithms have demonstrated a high ability to perform speedy and accurate COVID-19 diagnosis utilizing computed tomography (CT) and X-Ray scans. The spatial information in these images was used to train DL models in the majority of relevant studies. However, training these models with images generated by radiomics approaches could enhance diagnostic accuracy. Furthermore, combining information from several radiomics approaches with time-frequency representations of the COVID-19 patterns can increase performance even further. This study introduces "RADIC", an automated tool that uses three DL models that are trained using radiomics-generated images to detect COVID-19. First, four radiomics approaches are used to analyze the original CT and X-ray images. Next, each of the three DL models is trained on a different set of radiomics, X-ray, and CT images. Then, for each DL model, deep features are obtained, and their dimensions are decreased using the Fast Walsh Hadamard Transform, yielding a time-frequency representation of the COVID-19 patterns. The tool then uses the discrete cosine transform to combine these deep features. Four classification models are then used to achieve classification. In order to validate the performance of RADIC, two benchmark datasets (CT and X-Ray) for COVID-19 are employed. The final accuracy attained using RADIC is 99.4% and 99% for the first and second datasets respectively. To prove the competing ability of RADIC, its performance is compared with related studies in the literature. The results reflect that RADIC achieve superior performance compared to other studies. The results of the proposed tool prove that a DL model can be trained more effectively with images generated by radiomics techniques than the original X-Ray and CT images. Besides, the incorporation of deep features extracted from DL models trained with multiple radiomics approaches will improve diagnostic accuracy.

Using machine learning to improve our understanding of COVID-19 infection in children

PURPOSE

Children are at elevated risk for COVID-19 (SARS-CoV-2) infection due to their social behaviors. The purpose of this study was to determine if usage of radiological chest X-rays impressions can help predict whether a young adult has COVID-19 infection or not.

METHODS

A total of 2572 chest impressions from 721 individuals under the age of 18 years were considered for this study. An ensemble learning method, Random Forest Classifier (RFC), was used for classification of patients suffering from infection.

RESULTS

Five RFC models were implemented with incremental features and the best model achieved an F1-score of 0.79 with Area Under the ROC curve as 0.85 using all input features. Hyper parameter tuning and cross validation was performed using grid search cross validation and SHAP model was used to determine feature importance. The radiological features such as pneumonia, small airways disease, and atelectasis (confounded with catheter) were found to be highly associated with predicting the status of COVID-19 infection.

CONCLUSIONS

In this sample, radiological X-ray films can predict the status of COVID-19 infection with good accuracy. The multivariate model including symptoms presented around the time of COVID-19 test yielded good prediction score.

SARS-COV-2 Infection in Children in Emergency Departments in Spain: A Multicenter Study

OBJECTIVES

The first cases of infection by SARS-CoV-2 in the Spanish pediatric population were reported on early March 2020. Although most were mild or asymptomatic, new forms of clinical presentation and severity were reported with the evolution of the pandemic. We aimed to describe demographics, clinical features, and management of children with COVID-19 treated in Spanish emergency departments (EDs).

METHODS

A multicenter registry including 15 pediatric EDs was carried out. Patients younger than 18 years with confirmed acute SARS-CoV2 infection diagnosed between March and August 2020 were included.

RESULTS

Three hundred ninety-five patients were analyzed (median age, 4.3 years). Fifty-five (13.9%) had comorbidities, and 141 (35.7%) a household contact with confirmed COVID-19. The most reported symptoms were fever (85.2%) and cough (41.7%). Fifty (12.5%) were asymptomatic. Seventeen (4.9%) were not well-appearing at presentation. Children underwent a blood test in 26.7% and a chest X-ray in 21.4%; findings were often unremarkable. Symptomatic treatment was prescribed to 80%; 6 (1.7%) received antiviral treatment. Seventy-one (20.6%) were hospitalized, and 3 (0.9%) were admitted to the intensive care unit; no patient died. The main clinical diagnoses were fever without a source (38%) and upper respiratory tract infection (32.2%); 4 (1.1%) presented a multisystem inflammatory syndrome.

CONCLUSIONS

Most pediatric COVID-19 cases in EDs during the first months of the pandemic were healthy, well-appearing children, presenting with fever +/- respiratory symptoms. In a significant number of cases, there was household transmission. Most children were managed as outpatients with symptomatic treatment, being exceptional the evolution to a serious illness.

Predictors of severity in severe respiratory infection in children with COVID-19 respiratory infection in a developing country

On March 11, 2020, the WHO declared the COVID-19 pandemic. This name was given to the disease caused by the SARS-CoV 2 virus at its outbreak in December 2019 in Wuhan, Hubei, China. In Colombia, a significant number of cases have been confirmed. The aim of this study was to evaluate children with respiratory symptoms caused by SARS-CoV2 infection, identifying independent predictors of risk of having a severe illness, thus leading to an early approach and intervention in our patients, especially in children with comorbidities. An analytical cross-sectional study was conducted between April 1, 2020 and March 31, 2021 at a fourth-level referral institution in Bogotá on patients under 18 years of age with respiratory symptoms and a COVID-19 diagnosis confirmed in the laboratory. An explanatory binary logistic regression model was performed with an outcome variable of admission to the intensive care unit. A total of 385 children were included in the study, with ages between 9 months and 17 years of age; 50.1% were male, and the ICR was 9.75 years. 41.6% had some comorbidity, 13.5% were admitted to the pediatric ICU, and 3.6% of the total number of patients died. The predictor variables were: use of antibiotics in the first 24 h, neurological comorbidity, and consolidation shown in the chest X-ray. This explains 38.7% of the variability of the variable. In this cohort of patients with COVID-19-associated respiratory symptoms, we identified predictors of severity, so we consider that these patients require a risk approach that allows timely and adequate care.

Radiographic Features of COVID-19 in Children—A Systematic Review

INTRODUCTION: The SARS-CoV-19 (COVID-19) pandemic has become a global problem but has affected the paediatric population less so than in adults. The clinical picture in paediatrics can be different to adults but nonetheless both groups have been subject to frequent imaging. The overall aim of this study was to comprehensively summarise the findings of the available literature describing the chest radiograph (CXR) findings of paediatric patients with confirmed COVID-19. The COVID-19 landscape is rapidly changing, new information is being constantly brought to light, it is therefore important to appraise clinicians and the wider scientific community on the radiographic features of COVID-19 in children. METHODS: Four databases, which included, PubMed; Medline; CINAHL; ScienceDirect were searched from the 30 November 2020 to the 5 March 2021. The review was conducted using the “Preferred Reporting Items for Systematic Reviews and Meta-Analysis, PRISMA” guidelines. Studies were included for (1) publications with full text available, (2) patients with confirmed COVID-19 diagnoses, (3) CXR imaging features of COVID-19 were reported, (4) the age of patients was 0–18 years, (5) studies were limited to human subjects and (6) a language restriction of English was placed on the search. Quality assessment of included articles used the National of Health Quality Assessment Tool for Case Series Studies. RESULTS: Eight studies met our criteria for inclusion in the review. All eight studies documented the number of CXRs obtained, along with the number of abnormal CXRs. Seven out of the eight studies noted greater than 50% of the CXRs taken were abnormal. Opacification was the number one feature that was recorded in all eight studies, followed by pleural effusion which was seen in six studies. Consolidation and peri-bronchial thickening features were both evident in four studies. Opacification was sub-divided into common types of opacities i.e., consolidation, ground glass opacities, interstitial, alveolar and hazy. Consolidation was reported in half of the studies followed by ground glass opacities and interstitial opacities which was seen in three out of the eight studies. CONCLUSION: This systematic review provides insight into the common COVID-19 features that are seen on CXRs in paediatric patients. Opacification was the most common feature reported, with consolidation, ground glass and interstitial opacities the top three opacifications seen. Peri-bronchial thickening is reported. in the paediatric population but this differs from the adult population and was not reported as a common radiographic finding typically seen in adults. ADVANCES IN KNOWLEDGE: This systematic review highlights the CXR appearances of paediatric patients with confirmed SARS-CoV-19, to gain insight into the disease pathophysiology and provide a comprehensive summary of the features for clinicians aiding optimal management.

Obesity is a risk factor for decrease in lung function after COVID-19 infection in children with asthma

INTRODUCTION

It is not clear whether asthma, the most frequent chronic disease in childhood, is a risk for severe SARS-CoV-2 infection in the pediatric population and how SARS-CoV-2 infection affects the lung functions in these patients.

PURPOSE

We aimed to investigate the course and the consequences of SARS-CoV-2 infection among children with asthma and determine the risk factors for the decline in lung function tests (LFTs).

METHODS

In this retrospective study, asthmatic children with coronavirus disease 2019 (COVID-19) were compared with a random control group of asthmatic patients without COVID-19. In addition, the clinical course and the effect on LFTs of COVID-19 among children with asthma were also evaluated.

RESULTS

One hundred eighty-nine patients who had COVID-19, and 792 who did not were included in the study. Fever, fatigue, and cough were the most frequent symptoms during COVID-19. Regarding the severity of COVID-19, 163 patients (87.6%) had a mild clinical condition, 13 (7%) had moderate disease, 1 (0.5%) had severe disease, and 2 had (1.1%) critically ill disease. Two patients were diagnosed with multisystem inflammatory syndrome in children (MIS-C), one patient suffered from pneumothorax. LFTs of the patients before and after COVID-19 infection were analyzed; no significant differences were found in FEV₁ % (91.7% vs. 90.9%, p = 0.513), FVC% (89.8% vs. 90.8%, p = 0.502) and FEV₁ /FVC (103.1% vs. 100.6%, p = 0.056), while FEF25%-75% values (107.6% vs. 98.4%, p < 0.001) were significantly lower after the COVID-19 infection. Obesity (odds ratio [OR]: 3.785, 95% confidence interval [CI]: 1.152-12.429, p = 0.028] and having a family history of atopy (OR: 3.359, 95% CI: 1.168-9.657, p = 0.025] were found to be the independent risk factors for ≥25% decrease in FEF25-75 after COVID-19 infection.

CONCLUSION

COVID-19 infection leads to dysfunction of the small airways in asthmatic children and obesity is an independent risk factor for a ≥25% decrease in FEF25-75. The long-term effects of COVID-19 infection especially on small airways require close monitoring in children with asthma.

COVID-19 Chest X-ray Classification and Severity Assessment Using Convolutional and Transformer Neural Networks

The coronavirus pandemic started in Wuhan, China in December 2019, and put millions of people in a difficult situation. This fatal virus spread to over 227 countries and the number of infected patients increased to over 400 million cases, causing over 6 million deaths worldwide. Due to the serious consequence of this virus, it is necessary to develop a detection method that can respond quickly to prevent the spreading of COVID-19. Using chest X-ray images to detect COVID-19 is one of the promising techniques; however, with a large number of COVID-19 infected cases every day, the number of radiologists available to diagnose the chest X-ray images is not sufficient. We must have a computer aid system that helps doctors instantly and automatically determine COVID-19 cases. Recently, with the emergence of deep learning methods applied for medical and biomedical uses, using convolutional neural net and transformer applications for chest X-ray images can be a supplement for COVID-19 testing. In this paper, we attempt to classify three types of chest X-ray, which are normal, pneumonia, and COVID-19 using deep learning methods on a customized dataset. We also carry out an experiment on the COVID-19 severity assessment task using a tailored dataset. Five deep learning models were obtained to conduct our experiments: DenseNet121, ResNet50, InceptionNet, Swin Transformer, and Hybrid EfficientNet-DOLG neural networks. The results indicated that chest X-ray and deep learning could be reliable methods for supporting doctors in COVID-19 identification and severity assessment tasks.

COVID-19 smart diagnosis in the Emergency Department: all-in in practice

Introduction

Coronavirus Disease 19 (COVID-19) diagnosis has been a major problem in most Emergency Departments (EDs) and other senior care facilities. Various clinical manifestations, and the several radiologic and laboratory data combined with the misleading test results to identify the virus, are responsible for certain misdiagnoses, especially in suspected cases needing urgent management and treatment. Although emergency and other front-line physicians struggle to manage COVID-19 patients, still existent cases with ambiguous diagnosis trammel the ED safety and responsibility.

Areas Covered

This review article summarizes on a large scale the common information for the medical history, clinical examinations, radiology and laboratory data for SARS-CoV-2. We summarize the available literature using the PubMed, Science Direct and EMBASE databases published until December 2021 on the general information for COVID-19 diagnosis, and, finally, we propose algorithms for a precise and on-the-spot diagnosis the disease.

Expert Opinion

COVID-19 diagnosis has appeared to be such ambiguous, and physicians need to correlate medical history, medical examination, potential extrapulmonary manifestations, along with laboratory and radiologic data, for a prompt COVID-19 diagnosis.

COVID-19 in Pediatrics: Demographic, Clinical, Laboratory, and Radiological Characteristics of Infected Patients With SARS-CoV-2

The COVID-19 disease usually leads to mild infectious disease in children, but some develop serious complications. Here, we describe the characteristics of children with COVID-19 in northern Iran, the Golestan province. Ninety-one confirmed cases were enrolled in the study, aged 0-18 years. Demographic, clinical, comorbidity, laboratory, and radiological data were compared based on the disease severity (admitted to intensive care unit (ICU) or not) and disease outcome (recovered or deceased). Sixteen (17.5%) cases were hospitalized in ICU, and 8/91 (8.8%) deceased. Fever and cough were the most common clinical symptoms. Among all symptoms notified there were no significant differences between severe and milder cases, or between those who deceased and recovered. Failure to thrive (FTT), malignant disease and neurological disease were significantly more prevalent in severe cases as was frequently reported comorbidities. Laterality, ground-glass opacity, and lung consolidation were the most common findings in chest computed tomography. The data confirms that the COVID-19 disease has various presentations in children, and clinical, laboratory, and radiological findings may help predict the development of severe forms of COVID-19 among children.

Imaging findings in acute pediatric coronavirus disease 2019 (COVID-19) pneumonia and multisystem inflammatory syndrome in children (MIS-C)

The two primary manifestations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in children are acute coronavirus disease 2019 (COVID-19) pneumonia and multisystem inflammatory syndrome (MIS-C). While most pediatric cases of acute COVID-19 disease are mild or asymptomatic, some children are at risk for developing severe pneumonia. In MIS-C, children present a few weeks after SARS-CoV-2 exposure with a febrile illness that can rapidly progress to shock and multiorgan dysfunction. In both diseases, the clinical and laboratory findings can be nonspecific and present a diagnostic challenge. Thoracic imaging is commonly obtained to assist with initial workup, assessment of disease progression, and guidance of therapy. This paper reviews the radiologic findings of acute COVID-19 pneumonia and MIS-C, highlights the key distinctions between the entities, and summarizes our understanding of the role of imaging in managing SARS-CoV-2-related illness in children.

COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning

One of the primary clinical observations for screening the novel coronavirus is capturing a chest x-ray image. In most patients, a chest x-ray contains abnormalities, such as consolidation, resulting from COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from several sources are collected, and one of the largest publicly accessible datasets is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized to develop COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system.

Chest X-ray lung imaging features in pediatric COVID-19 and comparison with viral lower respiratory infections in young children

RATIONALE

Chest radiography (CXR) is a noninvasive imaging approach commonly used to evaluate lower respiratory tract infections (LRTIs) in children. However, the specific imaging patterns of pediatric coronavirus disease 2019 (COVID-19) on CXR, their relationship to clinical outcomes, and the possible differences from LRTIs caused by other viruses in children remain to be defined.

METHODS

This is a cross-sectional study of patients seen at a pediatric hospital with polymerase chain reaction (PCR)-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (n = 95). Patients were subdivided in infants (0-2 years, n = 27), children (3-10 years, n = 27), and adolescents (11-19 years, n = 41). A sample of young children (0-2 years, n = 68) with other viral lower respiratory infections (LRTI) was included to compare their CXR features with the subset of infants (0-2 years) with COVID-19.

RESULTS

Forty-five percent of pediatric patients with COVID-19 were hospitalized and 20% required admission to intensive care unit (ICU). The most common abnormalities identified were ground-glass opacifications (GGO)/consolidations (35%) and increased peribronchial markings/cuffing (33%). GGO/consolidations were more common in older individuals and perihilar markings were more common in younger subjects. Subjects requiring hospitalization or ICU admission had significantly more GGO/consolidations in CXR (p < .05). Typical CXR features of pediatric viral LRTI (e.g., hyperinflation) were more common in non-COVID-19 viral LRTI cases than in COVID-19 cases (p < .05).

CONCLUSIONS

CXR may be a complemental exam in the evaluation of moderate or severe pediatric COVID-19 cases. The severity of GGO/consolidations seen in CXR is predictive of clinically relevant outcomes. Hyperinflation could potentially aid clinical assessment in distinguishing COVID-19 from other types of viral LRTI in young children.

Spontaneous Massive Pneumomediastinum in a Previously Well Infant With COVID-19

A 3-month-old boy presented with a 3-hour history of a neck lump and difficulty breathing after 5 days of fever and reduced feeding. Pneumomediastinum with subcutaneous emphysema were identified, and the child was intubated because of severe work of breathing, requiring significant levels of oxygen and ventilatory pressure. Computed tomography chest scan revealed massive pneumomediastinum and significant bilateral parenchymal disease. The child deteriorated cardiovascularly, so the mediastinum was dissected by cardiothoracic surgeons and 2 drains were placed. The patient clinically improved with resolution of air leak over 2 days. A diagnosis of coronavirus disease 2019 pneumonia was confirmed.

The Role and Challenges of Clinical Imaging During COVID-19 Outbreak

Objective:

The Radiology department played a crucial role in detecting and following up with the COVID-19 disease during the pandemic. The purpose of this review was to highlight and discuss the role of each imaging modality, in the radiology department, that can help in the current pandemic and to determine the challenges faced by staff and how to overcome them.

Materials and Methods:

A literature search was performed using different databases, including PubMed, Google scholar, and the college electronic library to access 2020 published related articles.

Results:

A chest computed tomogram (CT) was found to be superior to a chest radiograph, with regards to the early detection of COVID-19. Utilizing lung point of care ultrasound (POCUS) with pediatric patients, demonstrated excellent sensitivity and specificity, compared to a chest radiography. In addition, lung ultrasound (LUS) showed a high correlation with the disease severity assessed with CT. However, magnetic resonance imaging (MRI) has some limiting factors with regard to its clinical utilization, due to signal loss. The reported challenges that the radiology department faced were mainly related to infection control, staff workload, and the training of students.

Conclusion:

The choice of an imaging modality to provide a COVID-19 diagnosis is debatable. It depends on several factors that should be carefully considered, such as disease stage, mobility of the patient, and ease of applying infection control procedures. The pros and cons of each imaging modality were highlighted, as part of this review. To control the spread of the infection, precautionary measures such as the use of portable radiographic equipment and the use of personal protective equipment (PPE) must be implemented.

A Promising and Challenging Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19

Chest X-rays (CXR) and computed tomography (CT) are the main medical imaging modalities used against the increased worldwide spread of the 2019 coronavirus disease (COVID-19) epidemic. Machine learning (ML) and artificial intelligence (AI) technology, based on medical imaging fully extracting and utilizing the hidden information in massive medical imaging data, have been used in COVID-19 research of disease diagnosis and classification, treatment decision-making, efficacy evaluation, and prognosis prediction. This review article describes the extensive research of medical image-based ML and AI methods in preventing and controlling COVID-19, and summarizes their characteristics, differences, and significance in terms of application direction, image collection, and algorithm improvement, from the perspective of radiologists. The limitations and challenges faced by these systems and technologies, such as generalization and robustness, are discussed to indicate future research directions.

Clinical Characteristics and Outcomes of a Cohort of Pediatric Oncohematologic Patients With COVID-19 Infection in the City of Bogotá, Colombia

BACKGROUND

In children, the complications of severe acute respiratory syndrome coronavirus 2 infection occur less frequently than in adults but the characteristics of this disease in oncology patients are not well characterized.

METHODS

This was a retrospective study in patients younger than 18 years of age with coronavirus disease 2019 (COVID-19) and cancer diagnoses between April and September 2020. Demographic variables, laboratory, and radiologic findings and complications of each case were identified. A descriptive analysis was performed.

RESULTS

A total of 33 patients were identified; the median age was 10 years. Fifteen patients (42%) were in chemotherapy at the time of the infection diagnosis, in two patients the chemotherapy protocol was permanently suspended. The most common symptom was fever in 20 patients (60%). Seven patients (21.2%) showed mild pneumonia, four patients (12.1%) severe pneumonia, and three cases (9.0%) were classified as critical. In the evaluated cohort, five patients (15.1%) died, and in two of those, death was caused by COVID-19 infection.

CONCLUSIONS

Children with an oncologic disease, the search for COVID cases should be oriented to patients with fever, including febrile neutropenia, the presence of respiratory symptoms, and the search for epidemiologic contact. A higher frequency of complications and mortality attributed to COVID-19, two in pediatric oncohematologic patients was found. Institutional strategies to detect the infection early and lower institutional infection are indicated.

Challenges and optimization strategies in medical imaging service delivery during COVID-19

In coronavirus disease 2019 (COVID-19), medical imaging plays an essential role in the diagnosis, management and disease progression surveillance. Chest radiography and computed tomography are commonly used imaging techniques globally during this pandemic. As the pandemic continues to unfold, many healthcare systems worldwide struggle to balance the heavy strain due to overwhelming demand for healthcare resources. Changes are required across the entire healthcare system and medical imaging departments are no exception. The COVID-19 pandemic had a devastating impact on medical imaging practices. It is now time to pay further attention to the profound challenges of COVID-19 on medical imaging services and develop effective strategies to get ahead of the crisis. Additionally, preparation for operations and survival in the post-pandemic future are necessary considerations. This review aims to comprehensively examine the challenges and optimization of delivering medical imaging services in relation to the current COVID-19 global pandemic, including the role of medical imaging during these challenging times and potential future directions post-COVID-19.

Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic

The advancement of technology remained an immersive interest for humankind throughout the past decades. Tech enterprises offered a stream of innovation to address the universal healthcare concerns. The novel coronavirus holds a substantial foothold of planet earth which is combatted by digital interventions across afflicted geographical boundaries and territories. This study aims to explore the trends of modern healthcare technologies and Artificial Intelligence (AI) during COVID-19 crisis, define the concepts and clinical role of AI in the mitigation of COVID-19, investigate and correlate the efficacy of AI-enabled technology in medical imaging during COVID-19 and determine advantages, drawbacks, and challenges of artificial intelligence during COVID-19 pandemic. The paper applied systematic review approach using a deliberated research protocol and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart. Digital technologies can coordinate COVID-19 responses in a cascade fashion that extends from the clinical care facility to the exterior of the pending viral epicenter. With cases of healthcare robotics, aerial drones, and the internet of things as evidentiary examples. PCR tests and medical imaging are the frontier diagnostics of COVID-19. Computed tomography helped to correct the accuracy variation of PCR tests at a clinical sensitivity of 98 %. Artificial intelligence can enable autonomous COVID-19 responses using techniques like machine learning. Technology could be an endless system of innovation and opportunities when sourced effectively. Scientists can utilize technology to resolve global concerns challenging the history of tangible possibility. Digital interventions have enhanced the responses to COVID-19, magnified the role of medical imaging amid the COVID-19 crisis and have exposed healthcare professionals to the opportunity of contactless care.

Cardio-thoracic imaging and COVID-19 in the pediatric population: A narrative review

Worldwide experience about coronavirus disease 2019 (COVID-19) pandemics suggests that symptomatic disease is significantly less frequent in the pediatric age range. Nevertheless, multi-system inflammatory syndrome has been consistently reported in children and has been associated with severe acute respiratory syndrome coronavirus 2 exposure. In this paper we give an overview of the multimodality chest imaging of pediatric patients with suspected COVID-19, focusing on relevant differences with adults.

Pediatric radiologic manifestations of COVID-19

Purpose

While full description of pediatric COVID-19 manifestations is evolving, children appear to present less frequently, and often display a less severe disease phenotype. There is correspondingly less data regarding pediatric radiologic findings. To describe the imaging findings of pediatric COVID-19, we evaluated the radiologic imaging of the initial patient cohort identified at our institution.

Methods

In this IRB approved study, all patients at our institution aged 0–21 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on PCR or immunoglobulin testing were identified. Imaging was reviewed by the co-authors and presence of abnormalities determined by consensus. Pre-existing comorbidities and alternative diagnoses were recorded. Rates of each finding were calculated. Findings were compared to published data following review of the available literature.

Results

Out of 130 Covid-19 positive patients, 24 patients underwent imaging, including 21 chest radiographs and 4 chest CT scans. Chest x-rays were normal in 33%. Patchy or streaky opacities were the most common radiographic abnormality, each seen in 38% of patients. CT findings included ill-defined or geographic ground glass opacities, dense opacities, septal thickening and crazy paving, and small pleural effusions. Results are similar to those reported in adults. Multiple COVID-19 positive children presented for symptoms due to an additional acute illness, including appendicitis and urinary infection.

Conclusions

Radiologic findings of COVID-19 in pediatric patients range from normal to severe ARDS type appearance. During this ongoing pandemic, these radiographic signs can be useful for the evaluation of disease status and guiding care, particularly in those with comorbidities.

Precis

Radiologic findings of COVID-19 in pediatric patients are similar to those seen in adults, and may range from normal to severe ARDS type appearance.

COVID-19 in Children: Respiratory Involvement and Some Differences With the Adults

The coronavirus disease 2019 (COVID-19) represents a health problem with multidimensional impacts and heterogeneous respiratory involvement in children, probably due to the interaction between different and complex mechanisms that could explain its variable degrees of severity. Although the majority of reports reveal that children develop less severe cases, the number of patients is increasing with more morbidity. Most serious respiratory manifestations are acute respiratory distress syndrome (ARDS) and pneumonia. By understanding the key aspects that can be used to differentiate between pediatric and adult respiratory compromise by COVID-19, we can improve our knowledge, and thus decrease the negative impact of the disease in the pediatric population. In this mini review, we summarize some of the mechanisms and findings that distinguish between adult and pediatric COVID-19 and respiratory involvement, taking into account some issues related to the physiopathology, diagnosis, clinical and paraclinical presentation, severity, treatment, and control of the disease.

Chest radiograph in hospitalized children with COVID-19. A review of findings and indications

PURPOSE

Many articles have been published regarding chest-imaging in COVID-19, but fewer studies have been published in pediatric populations. COVID-19 symptoms in children are generally milder and radiological tests have fewer positive findings. Indications for chest imaging in pediatric COVID-19 patients remain unclear. This study aims to describe the chest radiographs performed in COVID-19 patients in a pediatric hospital, to review the current chest X-ray indications and to develop an specific age-adjusted protocol for chest-imaging in children with COVID-19.

METHODS

Retrospective study in hospitalized pediatric COVID-19 patients in Navarre, Spain. Between March and December 2020, 44 children were included (mean age 3.8-year-old, 50 % males). Demographic information, cause of admission, symptoms, and clinical evolution were described. Chest imaging technique performed, indications and findings were analyzed. A literature review was performed searching for current COVID-19 pediatric chest-imaging indications.

RESULTS

Chest X-rays were performed in 35 patients (80 % of admissions) and most common indications were fever and respiratory symptoms. 53 % of the chest X-rays were considered "normal" and the classical bilateral diffuse interstitial pattern, described in adults, was only present in 22 %. All patients with pathological chest X-rays were symptomatic and reported fever (100 %) and fever tended to be longer (fever duration: 4.25 vs. 2.46 days p:0.048) in patients with pathological radiographs. We present a specific protocol for chest-imaging in pediatric COVID-19 cases.

CONCLUSIONS

COVID-19 clinical manifestations and radiological findings are milder and less specific in children. Imaging should not be used as a screening tool or a routine complementary test in pediatric COVID-19 patients, not even in hospitalized cases.

Lessons learned in transitioning to AI in the medical imaging of COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc across the world. It also created a need for the urgent development of efficacious predictive diagnostics, specifically, artificial intelligence (AI) methods applied to medical imaging. This has led to the convergence of experts from multiple disciplines to solve this global pandemic including clinicians, medical physicists, imaging scientists, computer scientists, and informatics experts to bring to bear the best of these fields for solving the challenges of the COVID-19 pandemic. However, such a convergence over a very brief period of time has had unintended consequences and created its own challenges. As part of Medical Imaging Data and Resource Center initiative, we discuss the lessons learned from career transitions across the three involved disciplines (radiology, medical imaging physics, and computer science) and draw recommendations based on these experiences by analyzing the challenges associated with each of the three associated transition types: (1) AI of non-imaging data to AI of medical imaging data, (2) medical imaging clinician to AI of medical imaging, and (3) AI of medical imaging to AI of COVID-19 imaging. The lessons learned from these career transitions and the diffusion of knowledge among them could be accomplished more effectively by recognizing their associated intricacies. These lessons learned in the transitioning to AI in the medical imaging of COVID-19 can inform and enhance future AI applications, making the whole of the transitions more than the sum of each discipline, for confronting an emergency like the COVID-19 pandemic or solving emerging problems in biomedicine.

Neumonía por COVID-19 en niños: De su etiología a su manejo

El COVID-19 es menos serio en niños que en adultos. Sin embargo, las afecciones respiratorias dominan el cuadro clínico de pacientes hospitalizados por COVID-19, aun en niños. En algunas series de casos, el deterioro del estado clínico, donde la disnea, la cianosis y el inicio del síndrome de dificultad respiratoria aguda (SDRA) emergieron ∼8–10 días después del inicio de la infección por SARS-CoV-2, pudo progresar rápidamente hasta la falla multiorgánica y la muerte. Esta revisión tiene como objetivo evaluar las características de la neumonía por COVID-19 en poblaciones pediátricas, comenzando con su etiología y sus mecanismos patológicos, para cerrar con su manejo clínico.

COVID-19 Pneumonia in Children: From Etiology to Management

COVID-19 is less serious in children than in adults. However, respiratory management dominates the clinical picture of hospitalized COVID-19 even in children. In some case series, deterioration of the clinical picture wherein dyspnea, cyanosis, and the onset of acute respiratory distress syndrome (ARDS) emerged ~8-10 days after the onset of SARS-CoV-2 infection, which could rapidly progress to multiple organ failure and death. This review aimed to evaluate the characteristics of COVID-19 pneumonia in pediatric populations, beginning from its etiology and pathological mechanisms and closing with its clinical management.