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Purpose: The portability of a hand-held ultrasound allows the health care worker to conduct lung ultrasound in out-of-hospital setting. It is used as a tool to conduct staging and triaging for COVID-19 patients. This study evaluated the utilization of lung ultrasound in an out-of-hospital setting versus chest x-rays in detecting and staging of COVID-19 patients with pneumonia., Methods: The study was conducted among COVID-19 subjects at an out-of-hospital setting whereby lung ultrasound was done and subsequently chest x-rays were taken after being admitted to the health care facilities. Lung ultrasound findings were reviewed by emergency physicians, while the chest x-rays were reviewed by radiologists. Radiologists were blinded by the patients' lung ultrasound findings and clinical conditions. The analysis of the agreement between the lung ultrasound findings and chest x-rays was conducted., Results: A total of 261 subjects were recruited. LUS detected pulmonary infiltrative changes in more stage 3 COVID-19 subjects in comparison to chest x-rays. Multiple B-lines were the predominant findings at the right lower anterior, posterior and lateral zones. Interstitial consolidations and ground glass opacities were the predominant descriptive findings in chest x-rays. However, there was no agreement between lung ultrasound and chest x-ray findings in detecting COVID-19 pneumonia as the Cohen's Kappa coefficient was 0.08 (95% CI 0.06-0.22, p = 0.16)., Conclusion: The diagnostic imaging and staging of COVID-19 patients using lung ultrasound in out-of-hospital settings showed LUS detected lung pleural disease more often than CXR for stage 3 COVID-19 patients., (© 2022. Società Italiana di Ultrasonologia in Medicina e Biologia (SIUMB).)

Abstract: Pneumonia is a common respiratory infection that affects the lungs. Lung ultrasound (LUS) is a portable, cost-effective imaging method, which is free of ionizing radiation and has been shown to be useful for evaluating pneumonia. The aim of this retrospective analytical study was to determine the association between lung ultrasound patterns and pneumonia. For the purpose of performing the required analysis, LUS patterns including consolidations, pleural line irregularities, A lines and B lines from 90 subjects (44 patients with confirmed pneumonia and 46 controls) were retrieved from a published open-access data set, which was reviewed and approved by medical experts. A χ 2 test was used for the comparison of categorical variables to determine the association between each LUS pattern and the presence of pneumonia. There is a significant association between LUS consolidation and the presence of pneumonia ( P < 0.0001). Lung ultrasound A lines are significantly associated with the absence of pneumonia ( P < 0.0001), whereas there are no associations between B lines or pleural line irregularities with pneumonia. Lung ultrasound consolidation is found to be associated with the presence of pneumonia. A lines are associated with healthy lungs, and there is no association of B lines and pleural irregularities with the presence of pneumonia. Further studies investigating LUS patterns with clinical information and symptoms of patients with pneumonia are required., Competing Interests: Conflict of Interest: none declared., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Pneumonia is one of the diseases that seriously endangers human health, and it is also the leading cause of death of children under the age of five in China. The most commonly used imaging examination method for radiologists is mainly based on chest X-ray images. Still, imaging errors often result during imaging examinations due to objective factors such as visual fatigue and lack of experience. Therefore, this paper proposes a feature fusion model, FC-VGG, based on the fusion of texture features (local binary pattern LBP and directional gradient histogram HOG) and depth features. The model improves model performance by adding detailed information in texture features to the convolutional neural network while making the model more suitable for clinical use. We input the X-ray image with texture features into the modified VGG16 model, C-VGG, and then add the Add fusion method to C-VGG for feature fusion so that FC-VGG is obtained, so FC-VGG has texture features detailed information and abstract information of deep features. Through experiments, our model has achieved 92.19% accuracy in recognizing children's pneumonia images, 93.44% average precision, 92.19% average recall, and 92.81% average F1 coefficient, and the model performance exceeds existing deep learning models and traditional feature recognition algorithms., Competing Interests: There are no competing interests associated with the manuscript., (Copyright © 2022 Hao-Nan Wang et al.)

The chest x-ray (CXR) was the gold standard in the diagnosis of pneumonia in children. However, CXR has limitations and cannot discriminate in etiology. Current guidelines recommend against routine use of CXR in children with uncomplicated lower respiratory tract infections (LRTI). We used routine care data from a multicentre RCT including 597 children with LRTI symptoms, to evaluate the influence of CXR on antibiotic prescription in the emergency department (ED). CXR remains frequently performed in non-complex children suspected of LRTI in the ED (18%). Children who underwent CXR were more likely to receive antibiotics, even when adjusted for symptoms, hospital and CXR results. Our study highlights the inferior role of CXR in treatment decisions for children with LRTI as CXR, regardless of its results, is independently associated with more antibiotic prescriptions.

The process of pneumonia detection has been the focus of researchers as it has proved itself to be one of the most dangerous and life-threatening disorders. In recent years, many machine learning and deep learning algorithms have been applied in an attempt to automate this process but none of them has been successful significantly to achieve the highest possible accuracy. In a similar attempt, we propose an enhanced approach of a deep learning model called restricted Boltzmann machine (RBM) which is named enhanced RBM (ERBM). One of the major drawbacks associated with the standard format of RBM is its random weight initialization which leads to improper feature learning of the model during the training phase, resulting in poor performance of the machine. This problem has been tried to eliminate in this work by finding the differences between the means of a specific feature vector and the means of all features given as inputs to the machine. By performing this process, the reconstruction of the actual features is increased which ultimately reduces the error generated during the training phase of the model. The developed model has been applied to three different datasets of pneumonia diseases and the results have been compared with other state of the art techniques using different performance evaluation parameters. The proposed model gave highest accuracy of 98.56% followed by standard RBM, SVM, KNN, and decision tree which gave accuracies of 97.53%, 92.62%, 91.64%, and 88.77%, respectively, for dataset named dataset 2. Similarly, for the dataset 1, the highest accuracy of 96.66 has been observed for the eRBM followed by srRBM, KNN, decision tree, and SVM which gave accuracies of 90.22%, 89.34%, 87.65%, and 86.55%, respectively. In the same way, the accuracies observed for the dataset 3 by eRBM, standard RBM, KNN, decision tree, and SVM are 92.45%, 90.98%, 87.54%, 85.49%, and 84.54%, respectively. Similar observations can also be seen for other performance parameters showing the efficiency of the proposed model. As revealed in the results obtained, a significant improvement has been observed in the working of the RBM by introducing a new method of weight initialization during the training phase. The results show that the improved model outperforms other models in terms of different performance evaluation parameters, namely, accuracy, sensitivity, specificity, F 1-score, and ROC curve., Competing Interests: No potential conflicts of interest have been declared by the authors., (Copyright © 2022 Fazli Wahid et al.)

Although Klebsiella pneumoniae pneumonia is an insidious threat among the elderly, the role of radiological features has not been elucidated. We aimed to evaluate thin-section chest computed tomography (CT) features and assess its associations with disease prognosis in elderly patients with acute K. pneumoniae pneumonia. We retrospectively included elderly patients, admitted for acute K. pneumoniae pneumonia, and investigated thin-section CT findings to determine whether bronchopneumonia or lobar pneumonia was present. The association between the radiological pattern of pneumonia and in-hospital mortality was analyzed. Eighty-six patients with acute K. pneumoniae pneumonia were included, and among them, the bronchopneumonia pattern was observed in 70 (81%) patients. Twenty-five (29%) patients died in hospital, and they had a greater incidence of lobar pneumonia pattern (40% in nonsurvivors vs 10% in survivors; P = .008), low albumin level (2.7 g/dL, range, 1.6-3.8 in nonsurvivors vs 3.0 g/dL, range, 1.7-4.2 in survivors; P = .026) and higher levels of aspartate aminotransferase (30 U/L, range, 11-186 in nonsurvivors vs 23 U/L, range, 11-102 in survivors, P = .017) and C-reactive protein (8.0 mg/dL, range, 0.9-26.5 in nonsurvivors vs 4.7 mg/dL, range, 0.0-24.0 in survivors; P = .047) on admission. Multivariate analysis showed that lobar pneumonia pattern was independently associated with increased in-hospital mortality (adjusted hazard ratio, 3.906; 95% CI, 1.513-10.079; P = .005). In elderly patients with acute K. pneumoniae pneumonia, the lobar pneumonia pattern may be less commonly observed, and this pattern could relate to poor prognosis., Competing Interests: The authors have no funding and conflicts of interest to disclose., (Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.)

The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled almost all countries. The pandemic has mounted pressure on the existing healthcare system and caused panic and desperation. The gold testing standard for COVID-19 detection, reverse transcription-polymerase chain reaction (RT-PCR), has shown its limitations with 70% accuracy, contributing to the incorrect diagnosis that exaggerated the complexities and increased the fatalities. The new variations further pose unseen challenges in terms of their diagnosis and subsequent treatment. The COVID-19 virus heavily impacts the lungs and fills the air sacs with fluid causing pneumonia. Thus, chest X-ray inspection is a viable option if the inspection detects COVID-19-induced pneumonia, hence confirming the exposure of COVID-19. Artificial intelligence and machine learning techniques are capable of examining chest X-rays in order to detect patterns that can confirm the presence of COVID-19-induced pneumonia. This research used CNN and deep learning techniques to detect COVID-19-induced pneumonia from chest X-rays. Transfer learning with fine-tuning ensures that the proposed work successfully classifies COVID-19-induced pneumonia, regular pneumonia, and normal conditions. Xception, Visual Geometry Group 16, and Visual Geometry Group 19 are used to realize transfer learning. The experimental results were promising in terms of precision, recall, F1 score, specificity, false omission rate, false negative rate, false positive rate, and false discovery rate with a COVID-19-induced pneumonia detection accuracy of 98%. Experimental results also revealed that the proposed work has not only correctly identified COVID-19 exposure but also made a distinction between COVID-19-induced pneumonia and regular pneumonia, as the latter is a very common disease, while COVID-19 is more lethal. These results mitigated the concern and overlap in the diagnosis of COVID-19-induced pneumonia and regular pneumonia. With further integrations, it can be employed as a potential standard model in differentiating the various lung-related infections, including COVID-19., Competing Interests: All the authors declare no conflicts of interest., (Copyright © 2022 Deepak Kumar Jain et al.)

Importance: The efficient and accurate interpretation of radiologic images is paramount., Objective: To evaluate whether a deep learning-based artificial intelligence (AI) engine used concurrently can improve reader performance and efficiency in interpreting chest radiograph abnormalities., Design, Setting, and Participants: This multicenter cohort study was conducted from April to November 2021 and involved radiologists, including attending radiologists, thoracic radiology fellows, and residents, who independently participated in 2 observer performance test sessions. The sessions included a reading session with AI and a session without AI, in a randomized crossover manner with a 4-week washout period in between. The AI produced a heat map and the image-level probability of the presence of the referrable lesion. The data used were collected at 2 quaternary academic hospitals in Boston, Massachusetts: Beth Israel Deaconess Medical Center (The Medical Information Mart for Intensive Care Chest X-Ray [MIMIC-CXR]) and Massachusetts General Hospital (MGH)., Main Outcomes and Measures: The ground truths for the labels were created via consensual reading by 2 thoracic radiologists. Each reader documented their findings in a customized report template, in which the 4 target chest radiograph findings and the reader confidence of the presence of each finding was recorded. The time taken for reporting each chest radiograph was also recorded. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were calculated for each target finding., Results: A total of 6 radiologists (2 attending radiologists, 2 thoracic radiology fellows, and 2 residents) participated in the study. The study involved a total of 497 frontal chest radiographs-247 from the MIMIC-CXR data set (demographic data for patients were not available) and 250 chest radiographs from MGH (mean [SD] age, 63 [16] years; 133 men [53.2%])-from adult patients with and without 4 target findings (pneumonia, nodule, pneumothorax, and pleural effusion). The target findings were found in 351 of 497 chest radiographs. The AI was associated with higher sensitivity for all findings compared with the readers (nodule, 0.816 [95% CI, 0.732-0.882] vs 0.567 [95% CI, 0.524-0.611]; pneumonia, 0.887 [95% CI, 0.834-0.928] vs 0.673 [95% CI, 0.632-0.714]; pleural effusion, 0.872 [95% CI, 0.808-0.921] vs 0.889 [95% CI, 0.862-0.917]; pneumothorax, 0.988 [95% CI, 0.932-1.000] vs 0.792 [95% CI, 0.756-0.827]). AI-aided interpretation was associated with significantly improved reader sensitivities for all target findings, without negative impacts on the specificity. Overall, the AUROCs of readers improved for all 4 target findings, with significant improvements in detection of pneumothorax and nodule. The reporting time with AI was 10% lower than without AI (40.8 vs 36.9 seconds; difference, 3.9 seconds; 95% CI, 2.9-5.2 seconds; P < .001)., Conclusions and Relevance: These findings suggest that AI-aided interpretation was associated with improved reader performance and efficiency for identifying major thoracic findings on a chest radiograph.

COVID-19 which was announced as a pandemic on 11 March 2020, is still infecting millions to date as the vaccines that have been developed do not prevent the disease but rather reduce the severity of the symptoms. Until a vaccine is developed that can prevent COVID-19 infection, the testing of individuals will be a continuous process. Medical personnel monitor and treat all health conditions; hence, the time-consuming process to monitor and test all individuals for COVID-19 becomes an impossible task, especially as COVID-19 shares similar symptoms with the common cold and pneumonia. Some off-the-counter tests have been developed and sold, but they are unreliable and add an additional burden because false-positive cases have to visit hospitals and perform specialized diagnostic tests to confirm the diagnosis. Therefore, the need for systems that can automatically detect and diagnose COVID-19 automatically without human intervention is still an urgent priority and will remain so because the same technology can be used for future pandemics and other health conditions. In this paper, we propose a modified machine learning (ML) process that integrates deep learning (DL) algorithms for feature extraction and well-known classifiers that can accurately detect and diagnose COVID-19 from chest CT scans. Publicly available datasets were made available by the China Consortium for Chest CT Image Investigation (CC-CCII). The highest average accuracy obtained was 99.9% using the modified ML process when 2000 features were extracted using GoogleNet and ResNet18 and using the support vector machine (SVM) classifier. The results obtained using the modified ML process were higher when compared to similar methods reported in the extant literature using the same datasets or different datasets of similar size; thus, this study is considered of added value to the current body of knowledge. Further research in this field is required to develop methods that can be applied in hospitals and can better equip mankind to be prepared for any future pandemics.

Tracheal bronchus, also known as bronchus suis , is a rare congenital anomaly of the airway where an accessory bronchus originates directly from the trachea. With an estimated incidence of 0.001%-2.0%, this condition is rarely reported in literature. It is usually discovered as an incidental finding in an otherwise asymptomatic individual. However, it can act as a focus of recurrent infection or present as persistent radiographic infiltrates. Multidetector CT imaging and bronchoscopy play a crucial role in the identification of this entity. We hereby report the case of a middle-aged man who presented with recurrent right upper lobe pneumonia, which was found to be due to an underlying tracheal bronchus., Competing Interests: Competing interests: None declared., (© BMJ Publishing Group Limited 2022. No commercial re-use. See rights and permissions. Published by BMJ.)

Pneumonia infection is the leading cause of death in young children. The commonly used pneumonia detection method is that doctors diagnose through chest X-ray, and external factors easily interfere with the results. Assisting doctors in diagnosing pneumonia in patients based on deep learning methods can effectively eliminate similar problems. However, the complex network structure and redundant parameters of deep neural networks and the limited storage and computing resources of clinical medical hardware devices make it difficult for this method to use widely in clinical practice. Therefore, this paper studies a lightweight pneumonia classification network, CPGResNet50 (ResNet50 with custom channel pruning and ghost methods), based on ResNet50 pruning and compression to better meet the application requirements of clinical pneumonia auxiliary diagnosis with high precision and low memory. First, based on the hierarchical channel pruning method, the channel after the convolutional layer in the bottleneck part of the backbone network layer is used as the pruning object, and the pruning operation is performed after its normalization to obtain a network model with a high compression ratio. Second, the pruned convolutional layers are decomposed into original convolutions and cheap convolutions using the optimized convolution method. The feature maps generated by the two convolution parts are combined as the input to the next convolutional layer. Further, we conducted many experiments using pneumonia X-ray medical image data. The results show that the proposed method reduces the number of parameters of the ResNet50 network model from 23.7 M to 3.455 M when the pruning rate is 90%, a reduction is more than 85%, FIOPs dropped from 4.12G to 523.09 M, and the speed increased by more than 85%. The model training accuracy error remained within 1%. Therefore, the proposed method has a good performance in the auxiliary diagnosis of pneumonia and obtained good experimental results., Competing Interests: There are no competing interests associated with the manuscript., (Copyright © 2022 Chao-Peng Yang et al.)

Pneumonia is one of the diseases that causes the most fatalities worldwide, especially in children. Recently, pneumonia-caused deaths have increased dramatically due to the novel Coronavirus global pandemic. Chest X-ray (CXR) images are one of the most readily available and common imaging modality for the detection and identification of pneumonia. However, the detection of pneumonia from chest radiography is a difficult task even for experienced radiologists. Artificial Intelligence (AI) based systems have great potential in assisting in quick and accurate diagnosis of pneumonia from chest X-rays. The aim of this study is to develop a Neural Architecture Search (NAS) method to find the best convolutional architecture capable of detecting pneumonia from chest X-rays. We propose a Learning by Teaching framework inspired by the teaching-driven learning methodology from humans, and conduct experiments on a pneumonia chest X-ray dataset with over 5000 images. Our proposed method yields an area under ROC curve (AUC) of 97.6% for pneumonia detection, which improves upon previous NAS methods by 5.1% (absolute)., (© 2022. The Author(s).)

Chest X-ray (CXR) scans are emerging as an important diagnostic tool for the early spotting of COVID and other significant lung diseases. The recognition of visual symptoms is difficult and can take longer time by radiologists as CXR provides various signs of viral infection. Therefore, artificial intelligence-based method for automated identification of COVID by utilizing X-ray images has been found to be very promising. In the era of deep learning, effective utilization of existing pretrained generalized models is playing a decisive role in terms of time and accuracy. In this paper, the benefits of weights of existing pretrained model VGG16 and InceptionV3 have been taken. Base model has been created using pretrained models (VGG16 and InceptionV3). The last fully connected (FC) layer has been added as per the number of classes for classification of CXR in binary and multi-class classification by appropriately using transfer learning. Finally, combination of layers is made by integrating the FC layer weights of both the models (VGG16 and InceptionV3). The image dataset used for experimentation consists of healthy, COVID, pneumonia viral, and pneumonia bacterial. The proposed weight fusion method has outperformed the existing models in terms of accuracy, achieved 99.5% accuracy in binary classification over 20 epochs, and 98.2% accuracy in three-class classification over 100 epochs., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Bhavana Kaushik et al.)

Aim: To evaluate the effectiveness of the systemic immune-inflammation index (SII) and other biomarkers in distinguishing parapneumonic effusion (PPE) and empyema., Methods: Patients who were thought to have pleural effusion secondary to pneumonia in the pediatric emergency department (PED) between 2004 and 2021 were retrospectively evaluated. The patients were divided into two groups as empyema and PPE. The efficacy of infection markers in predicting empyema was compared., Results: Fifty-nine patients (59.3% male) were included in the study. Forty-three (72.9%) patients were in the PPE and 16 (27.1%) were in the empyema group. Length of hospital stay and pleural fluid thickness measured with thoracic ultrasonography were significantly higher in the empyema group (p = 0.018 and p = 0.002, respectively). The mean SII was 1902.73 ± 1588.87 in PPE patients, while it was 6899.98 ± 6678 in empyema patients (p = 0.009). C-reactive protein (CRP), white blood cell (WBC) count, absolute neutrophil count (ANC), and neutrophil-lymphocyte ratio (NLR) ​​were significantly higher in the empyema group; absolute lymphocyte count (ALC) and lymphocyte-monocytes ratio (LMR) were significantly lower than the PPE group. When the best cut-off values of inflammation markers are determined according to the area under the curve, the highest odds ratios suggesting empyema were found in SII, LMR, CRP, and ANC, respectively., Conclusion: Inflammation markers can be useful in predicting empyema. The best markers were found to be SII, LMR, CRP, and ANC. High SII is one of the practical diagnostic markers that can be used differentiate empyema from PPE in PED., (© 2022 Wiley Periodicals LLC.)

Background: Parapneumonic effusions and empyema are the most frequent complication of pediatric pneumonia. Interventions include chest drain and fibrinolytics (CDF) or thoracoscopic surgery. CDF is considered less invasive, and more cost-effective though with higher rates of reintervention. We hypothesized that sonographic pleural fluid characteristics could identify cases at increased risk of reintervention following primary CDF., Methods: A retrospective cohort of complicated pneumonia managed with primary CDF (2011-2018). Cases were reviewed using ultrasound criteria to describe pleural fluid. We analyzed the correlation between ultrasound findings and reintervention., Results: We report 129 cases with a median age of 3.8 years and 44% female. A repeat intervention occurred for 24/129 (19%) cases. The interobserver reliability was moderate for the number of septations (κ 0.72, 95% CI [confidence interval]: 0.62-0.81), weak for the size of the largest locule (κ 0.55, 95% CI: 0.44-0.67), and minimal for the level of echogenicity (κ 0.24, 95% CI: 0.11-0.37), pleural thickening (κ 0.29, 95% CI: 0.17-0.42), maximum effusion depth (κ 0.37, 95% CI: 0.22-0.51), and radiologist's risk for reintervention (κ 0.34, 95% CI: 0.18-0.5). A repeat intervention was not associated with any objective sonographic variable., Conclusion: We report no association between ultrasound characteristics and repeat intervention for complicated pneumonia following primary CDF treatment. There was minimal interobserver agreement in reporting ultrasound characteristics despite more objective criteria. Clinicians rely on ultrasound findings to support decisions around intervention in pediatric empyema. This study does not support relying on ultrasound to estimate the likelihood of reintervention., (© 2022 Wiley Periodicals LLC.)

Bovine respiratory disease (BRD) continues to be a leading cause of economic loss, hampered animal welfare, and intensive antimicrobial use in cattle operations worldwide. Reduction of antimicrobial use is hindered because it is still unclear which clinical signs are best monitored to reliably detect pneumonia. Also, these clinical signs may vary according to age and between breeds. The objective of this cross-sectional study was to identify clinical signs associated with ultrasound-confirmed pneumonia (lung consolidation ≥1 cm depth) pre- and postweaning in different production types (dairy, beef, and veal) and breeds. A total of 956 calves (70% Holstein-Friesian dairy and 30% Belgian Blue beef) from 84 herds were clinically examined using 24 parameters, scored using the Wisconsin and California BRD clinical scoring systems and subjected to thoracic ultrasonography. Of the calves, 42.8% and 19.5% had a lung consolidation ≥1 cm and ≥3 cm, respectively. Cough, both spontaneous and induced, was the only and best-performing clinical sign statistically associated with lung consolidation in all production types. Fever (rectal temperature ≥39.4°C) was the second most promising factor, being significant in beef and veal calves but not in dairy calves. Postweaning, none of the clinical signs studied were statistically associated with pneumonia, with the exception of cough in dairy calves. Spontaneous or induced cough as a single clinical sign outperformed any combination of clinical signs, including the Wisconsin and California respiratory disease scoring systems, but sensitivity remained low. This information can be useful to select appropriate clinical signs for continuous monitoring in precision livestock applications, targeted to a given breed and age. As a cross-sectional measurement, diagnostic accuracy of spontaneous cough (accuracy = 65.1%, sensitivity = 37.4%, specificity = 85.7%) is too low to be used as a criterion to select animals with pneumonia for antimicrobial treatment. At the group level, cough monitoring holds potential as an early warning sign, after which lung ultrasonography should follow., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Background: Several prediction models have been reported to identify patients with radiographic pneumonia, but none have been validated or broadly implemented into practice. We evaluated 5 prediction models for radiographic pneumonia in children., Methods: We evaluated 5 previously published prediction models for radiographic pneumonia (Neuman, Oostenbrink, Lynch, Mahabee-Gittens, and Lipsett) using data from a single-center prospective study of patients 3 months to 18 years with signs of lower respiratory tract infection. Our outcome was radiographic pneumonia. We compared each model's area under the receiver operating characteristic curve (AUROC) and evaluated their diagnostic accuracy at statistically-derived cutpoints., Results: Radiographic pneumonia was identified in 253 (22.2%) of 1142 patients. When using model coefficients derived from the study dataset, AUROC ranged from 0.58 (95% confidence interval, 0.52-0.64) to 0.79 (95% confidence interval, 0.75-0.82). When using coefficients derived from original study models, 2 studies demonstrated an AUROC >0.70 (Neuman and Lipsett); this increased to 3 after deriving regression coefficients from the study cohort (Neuman, Lipsett, and Oostenbrink). Two models required historical and clinical data (Neuman and Lipsett), and the third additionally required C-reactive protein (Oostenbrink). At a statistically derived cutpoint of predicted risk from each model, sensitivity ranged from 51.2% to 70.4%, specificity 49.9% to 87.5%, positive predictive value 16.1% to 54.4%, and negative predictive value 83.9% to 90.7%., Conclusions: Prediction models for radiographic pneumonia had varying performance. The 3 models with higher performance may facilitate clinical management by predicting the risk of radiographic pneumonia among children with lower respiratory tract infection., (Copyright © 2022 by the American Academy of Pediatrics.)

Background: Acute respiratory distress syndrome (ARDS) is a challenging clinical problem. Discovering the potential metabolic alterations underlying the ARDS is important to identify novel therapeutic target and improve the prognosis. Serum and urine metabolites can reflect systemic and local changes and could help understanding metabolic characterization of community-acquired pneumonia (CAP) with ARDS., Methods: Clinical data of patients with suspected CAP at the First Affiliated Hospital of Wenzhou Medical University were collected from May 2020 to February 2021. Consecutive patients with CAP were enrolled and divided into two groups: CAP with and without ARDS groups. 1 H nuclear magnetic resonance-based metabolomics analyses of serum and urine samples were performed before and after treatment in CAP with ARDS (n = 43) and CAP without ARDS (n = 45) groups. Differences metabolites were identifed in CAP with ARDS. Furthermore, the receiver operating characteristic (ROC) curve was utilized to identify panels of significant metabolites for evaluating therapeutic effects on CAP with ARDS. The correlation heatmap was analyzed to further display the relationship between metabolites and clinical characteristics., Results: A total of 20 and 42 metabolites were identified in the serum and urine samples, respectively. Serum metabolic changes were mainly involved in energy, lipid, and amino acid metabolisms, while urine metabolic changes were mainly involved in energy metabolism. Elevated levels of serum 3-hydroxybutyrate, lactate, acetone, acetoacetate, and decreased levels of serum leucine, choline, and urine creatine and creatinine were detected in CAP with ARDS relative to CAP without ARDS. Serum metabolites 3-hydroxybutyrate, acetone, acetoacetate, citrate, choline and urine metabolite 1-methylnicotinamide were identified as a potential biomarkers for assessing therapeutic effects on CAP with ARDS, and with AUCs of 0.866 and 0.795, respectively. Moreover, the ROC curve analysis revealed that combined characteristic serum and urine metabolites exhibited a better classification system for assessing therapeutic effects on CAP with ARDS, with a AUC value of 0.952. In addition, differential metabolites strongly correlated with clinical parameters in patients with CAP with ARDS., Conclusions: Serum- and urine-based metabolomics analyses identified characteristic metabolic alterations in CAP with ARDS and might provide promising circulatory markers for evaluating therapeutic effects on CAP with ARDS., (© 2022. The Author(s).)

The aim is to solve the problem of the urgent need of a nonradiation, noninvasive, and simple-to-operate diagnostic method for neonatal pneumonia that can indicate the severity of the disease and dynamically monitor the outcome of the disease. The authors propose a bedside high-frequency ultrasound technique based on methods for evaluation in the detection and treatment of neonatal pneumonia. The results obtained are as follows: the sensitivity of neonatal lung ultrasound in the diagnosis of neonatal pneumonia was 96.6%, the specificity was 93.3%, the positive predictive value was 93.5%, and the negative predictive value was 96.5%. The sensitivity of chest X-ray in the diagnosis of neonatal pneumonia was 93.3%. Compared with the lung ultrasound and chest X-ray in the diagnosis of neonatal pneumonia, the two had a good correlation. The neonatal respiratory score was positively correlated with the lung ultrasound score, and the higher the lung ultrasound score, the more severe the disease. The score decreased by 35% after 3 days of treatment and 68% after 7 days of treatment, indicating that the lung high-frequency ultrasound score can be very effective in characterizing the treatment situation. It has been demonstrated that the lung ultrasound can be used as an imaging method for the diagnosis of neonatal pneumonia. The higher the lung ultrasound score, the more severe the disease, and the lung ultrasound score was positively correlated with the disease severity. With dynamic monitoring of the lung ultrasound and the gradual improvement of clinical symptoms after treatment, the lung ultrasound score gradually decreased; therefore, the lung ultrasound can be used for re-examination of neonatal pneumonia to evaluate the treatment effect and guidance., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Jianchang Zhu et al.)

Background: Diagnostic testing for bacterial etiology of community-acquired pneumonia (CAP) is insensitive. Induced sputum (IS) is an attractive option for the evaluation of the lower respiratory tract., Methods: Children aged 0-18 years with CAP were enrolled in the Etiology of Pneumonia in the Community (EPIC) study between 2010 and 2012. Blood and respiratory specimens were assessed by culture and polymerase chain reaction (PCR). The radiographic CAP was determined by a study radiologist. Sputum was induced with hypertonic saline. IS specimen was high quality (HQ) if Gram stain showed >25 white blood and <10 epithelial cells per low-powered field; all others were low quality (LQ). We compared IS pathogen prevalence between HQ and LQ IS, and by radiographic pneumonia. Pathogen concordance with EPIC etiology was assessed. Length of stay (LOS) was compared by receipt of IS pathogen-concordant antibiotics., Results: Out of 977 children, 916 (94%) children enrolled in Memphis, Tennessee, produced IS; 794 (87%) had radiographic CAP and 174 (19%) were HQ. HQ IS yielded pathogenic bacteria more often than LQ (64% vs 44%; P < .01); however, pathogens were isolated at similar rates in HQ IS in patients with and without radiographic CAP (64% vs. 63%; P = .6). Pathogens from study specimens matched an IS pathogen in only 9/42 (21%) patients with radiographic CAP. Median LOS was similar among patients with radiographic CAP regardless of receipt of IS pathogen-concordant antibiotics (3.1 days), non-pathogen-concordant antibiotics (2.7 days), or no antibiotics (3.2 days; P = .5)., Conclusions: Bacterial pathogens were isolated from most IS cultures regardless of radiographic CAP and quality of IS. IS cultures infrequently corresponded with sterile site cultures. Isolation of pathogens from pediatric IS reflects oropharyngeal carriage and is insufficient to determine bacterial etiology of CAP., (© The Author(s) 2022. Published by Oxford University Press on behalf of The Journal of the Pediatric Infectious Diseases Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The most popular test for pneumonia, a serious health threat to children, is chest X-ray imaging. However, the diagnosis of pneumonia relies on the expertise of experienced radiologists, and the scarcity of medical resources has forced us to conduct research on CAD (computer-aided diagnosis). In this study, we propose MP-ViT, the Multisemantic Level Patch Merger Vision Transformer, to achieve automatic diagnosis of pneumonia in chest X-ray images. We introduce Patch Merger to reduce the computational cost of ViT. Meanwhile, the intermediate results calculated by Patch Merger participate in the final classification in a concise way, so as to make full use of the intermediate information of the high-level semantic space to learn from local to overall and to avoid information loss caused by Patch Merger. We conducted experiments on a dataset with 3,883 chest X-ray images described as pneumonia and 1,349 images labeled as normal, and the results show that even without pretraining ViT on a large dataset, our model can achieve the accuracy of 0.91, the precision of 0.92, the recall of 0.89, and the F 1-score of 0.90, which is better than Patch Merger on a small dataset. The model can provide CAD for physicians and improve diagnostic reliability., Competing Interests: The authors declare that there is no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Zheng Jiang and Liang Chen.)

Acinetobacter baumannii (Ab) is an opportunistic Gram-negative pathogen intrinsically resistant to many antimicrobials. The aim of this retrospective study was to describe the imaging features on chest X-ray (CXR) and computed tomography (CT) scans in hospitalized patients with multidrug-resistant (MDR) Ab pneumonia. CXR and CT findings were graded on a three-point scale: 1 represents normal attenuation, 2 represents ground-glass attenuation, and 3 represents consolidation. For each lung zone, with a total of six lung zones in each patient, the extent of disease was graded using a five-point scale: 0, no involvement; 1, involving 25% of the zone; 2, 25−50%; 3, 50−75%; and 4, involving >75% of the zone. Points from all zones were added for a final total cumulative score ranging from 0 to 72. Among 94 patients who tested positive for MDR Ab and underwent CXR (males 52.9%, females 47.1%; mean age 64.2 years; range 1−90 years), 68 patients underwent both CXR and chest CT examinations. The percentage of patients with a positive CT score was significantly higher than that obtained on CXR (67.65% > 35.94%, p-value = 0.00258). CT score (21.88 ± 15.77) was significantly (p-value = 0.0014) higher than CXR score (15.06 ± 18.29). CXR and CT revealed prevalent bilateral abnormal findings mainly located in the inferior and middle zones of the lungs. They primarily consisted of peripheral ground-glass opacities and consolidations which predominated on CXR and CT, respectively.

Aspiration pneumonia is a common cause of morbidity and mortality in both adults and children that, however, is difficult to accurately diagnose. In current literature, there are no reports or clinical research study focused on the possible use of lung ultrasound (LUS) in the diagnosis and follow-up of aspiration pneumonia in children. In this case series, we describe clinical, laboratory, radiological results as well as detailed lung ultrasound findings of three children with severe disability and diagnosed with aspiration pneumonia. In these three cases, albeit at different times, LUS played an important role in both the initial diagnostic process and follow-up., (© 2020. Società Italiana di Ultrasonologia in Medicina e Biologia (SIUMB).)

Background: Immune checkpoint inhibitor-related pneumonia (ICIP) is an independent risk factor for immunotherapy-related death., Purpose: To evaluate the ICIP, dynamic observation of computed tomography (CT) images of lung cancer patients with ICIP was conducted to study the relationship between the occurrence of ICIP and clinical information., Material and Methods: CT images and clinical information of lung cancer patients (n = 76) from two hospitals who received immune checkpoint inhibitor (ICI) treatment were collected. A total of 49 cases were enrolled after screening according to the inclusion and exclusion criteria. We performed statistical analysis on the imaging features and clinical information., Results: Analysis of imaging characteristics revealed two types of ICIP: the limited-onset type and diffuse-onset type. The median onset time of limited-onset ICIP was significantly earlier than that of diffuse-onset ICIP (1.5 months vs. 2.8 months; p = 0.045). Statistical analysis based on differences within the group showed that the clinical ICIP grade and immunotherapy response rate of limited-onset cases were statistically significant (p = 0.003) and the imaging/clinical ICIP grade and the outcome of ICIP were statistically significant (p = 0.031/0.007). The immunotherapy strategy of diffuse-onset cases and the response rate of immunotherapy were statistically significant (p = 0.016)., Conclusions: This study suggests that pre-existing lung lesions can be one of the possible predisposing factors for ICIP and describes the development of ICIP through continuous imaging. Our findings indicate pre-existing lung lesions as a referential monitoring target for the onset and progression of ICIP for clinical practice., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Purpose: Imaging of acute lung inflammation is pivotal to evaluate innovative ventilation strategies. We aimed to develop and validate a three-tissue compartment kinetic model (3TCM) of [ 11 C](R)-PK11195 lung uptake in experimental acute respiratory distress syndrome (ARDS) to help quantify macrophagic inflammation, while accounting for the impact of its non-specific and irreversible uptake in lung tissues., Material and Methods: We analyzed the data of 38 positron emission tomography (PET) studies performed in 21 swine with or without experimental ARDS, receiving general anesthesia and mechanical ventilation. Model input function was a plasma, metabolite-corrected, image-derived input function measured in the main pulmonary artery. Regional lung analysis consisted in applying both the 3TCM and the two-tissue compartment model (2TCM); in each region, the best model was selected using a selection algorithm with a goodness-of-fit criterion. Regional best model binding potentials (BP ND ) were compared to lung macrophage presence, semi-quantified in pathology., Results: The 3TCM was preferred in 142 lung regions (62%, 95% confidence interval: 56 to 69%). BP ND determined by the 2TCM was significantly higher than the value computed with the 3TCM (overall median with interquartile range: 0.81 [0.44-1.33] vs. 0.60 [0.34-0.94], p < 0.02). Regional macrophage score was significantly associated with the best model BP ND (p = 0.03). Regional BP ND was significantly increased in the hyperinflated lung compartment, compared to the normally aerated one (median with interquartile range: 0.8 [0.6-1.7] vs. 0.6 [0.3-0.8], p = 0.03)., Conclusion: To assess the intensity and spatial distribution of acute macrophagic lung inflammation in the context of experimental ARDS with mechanical ventilation, PET quantification of [ 11 C](R)-PK11195 lung uptake was significantly improved in most lung regions using the 3TCM. This new methodology offers the opportunity to non-invasively evaluate innovative ventilatory strategies aiming at controlling acute lung inflammation., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: over the last year, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its variants have highlighted the importance of screening tools with high diagnostic accuracy for new illnesses such as COVID-19. In that regard, deep learning approaches have proven as effective solutions for pneumonia classification, especially when considering chest-x-rays images. However, this lung infection can also be caused by other viral, bacterial or fungi pathogens. Consequently, efforts are being poured toward distinguishing the infection source to help clinicians to diagnose the correct disease origin. Following this tendency, this study further explores the effectiveness of established neural network architectures on the pneumonia classification task through the transfer learning paradigm., Methodology: to present a comprehensive comparison, 12 well-known ImageNet pre-trained models were fine-tuned and used to discriminate among chest-x-rays of healthy people, and those showing pneumonia symptoms derived from either a viral (i.e., generic or SARS-CoV-2) or bacterial source. Furthermore, since a common public collection distinguishing between such categories is currently not available, two distinct datasets of chest-x-rays images, describing the aforementioned sources, were combined and employed to evaluate the various architectures., Results: the experiments were performed using a total of 6330 images split between train, validation, and test sets. For all models, standard classification metrics were computed (e.g., precision, f1-score), and most architectures obtained significant performances, reaching, among the others, up to 84.46% average f1-score when discriminating the four identified classes. Moreover, execution times, areas under the receiver operating characteristic (AUROC), confusion matrices, activation maps computed via the Grad-CAM algorithm, and additional experiments to assess the robustness of each model using only 50%, 20%, and 10% of the training set were also reported to present an informed discussion on the networks classifications., Conclusion: this paper examines the effectiveness of well-known architectures on a joint collection of chest-x-rays presenting pneumonia cases derived from either viral or bacterial sources, with particular attention to SARS-CoV-2 contagions for viral pathogens; demonstrating that existing architectures can effectively diagnose pneumonia sources and suggesting that the transfer learning paradigm could be a crucial asset in diagnosing future unknown illnesses., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Study Objective: The COVID-19 pandemic in the United States has underscored the need to understand health care in a regional context. However, there are multiple definitions of health care regions available for conducting geospatial analyses. In this study, we compare the novel Pittsburgh Atlas, which defined regions for emergency care, with the existing definitions of regions, counties, and the Dartmouth Atlas, with respect to nonemergent acute medical conditions using pneumonia admissions., Methods: We identified patients hospitalized with a primary diagnosis of pneumonia or a primary admitting diagnosis of sepsis with a secondary diagnosis of pneumonia in the Agency for Healthcare Research and Quality's State Inpatient Databases. We calculated the percentage of region concordant care, the localization index, and market share for 3 definitions of health care regions (the Pittsburgh Atlas, Dartmouth Atlas, and counties). We used logistic regression identified predictors of region concordant care., Results: We identified 1,582,287 patients who met the inclusion criteria. We found that the Pittsburgh Atlas and Dartmouth Atlas definitions of regions performed similarly with respect to both localization index (92.0 [interquartile range 87.9 to 95.7] versus 90.3 [interquartile range 81.4 to 94.5]) and market share (8.5 [interquartile range 5.1 to 13.6] versus 9.4 [interquartile range 6.7 to 14.1]). Both atlases outperformed the localization index (67.5 [interquartile range 49.9 to 83.9]) and market share (20.0% [interquartile range 11.4 to 31.4]) of the counties. Within a given referral region, the demographic factors, including age, sex, race/ethnicity, insurance status, and the level of severity, affected concordance rates between residential and hospital regions., Conclusion: Because the Pittsburgh Atlas also has the benefit of respecting state and county boundaries, the use of this definition may have improved policy applicability without sacrificing accuracy in defining health care regions for acute medical conditions., (Copyright © 2021 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.)

Round pneumonia is a radiological manifestation of pulmonary lesion. This is found as spherical or oval-shaped radio-opacity on chest X-ray. Round pneumonia has been reported in literature uncommonly. Round pneumonia was first time reported in the radiology literature in 1954 (Wagner et al., 1998). It was first recognized in children. In 1973, Rose and Ward reviewed 21 cases of round pneumonia in children. Radiological findings resembled pulmonary and mediastinal masses. Since then, time and again, round pneumonia has been reported in children; but, this is also found rarely in adults. There are many causes of round pneumonia in adults, for example, infectious and noninfectious. It may mimic pulmonary neoplasms due to its radiological appearance. Hence, the usual diagnostic challenge of round pneumonia is to differentiate pneumonia from bronchogenic carcinoma. Here we present an interesting case of round pneumonia in an adult female., (© Journal of the Association of Physicians of India 2011.)

Background/objectives: Chest radiograph (CXR) is routinely performed among children with suspected pneumonia, though it is not clear how specific radiographic findings impact antibiotic treatment for pneumonia. We evaluated the impact of viral radiographic features on antibiotic treatment among children undergoing pneumonia evaluation in the emergency department (ED)., Methods: Children presenting to a pediatric ED who underwent a CXR for pneumonia evaluation were prospectively enrolled. Prior to CXR performance, physicians indicated their level of suspicion for pneumonia. The CXR report was reviewed to assess for the presence of viral features (peribronchial cuffing, perihilar markings, and interstitial infiltrate) as well as radiographic features suggestive of pneumonia (consolidation, infiltrate, and opacity). The relationship between viral radiographic features and antibiotic treatment was assessed based on the level of clinical suspicion for pneumonia prior to CXR., Results: Patients with normal CXRs (n = 400) and viral features alone (n = 370) were managed similarly, with 8.0% and 8.6% of patients receiving antibiotic treatment, respectively (P = .75). Compared with children with radiographic pneumonia (n = 174), patients with concurrent viral features and radiographic pneumonia (n = 177) were treated with antibiotics less frequently (86.2% vs 54.3%, P < .001). Among children with isolated viral features on CXR, antibiotic treatment rates were correlated with pre-CXR level of suspicion for pneumonia., Conclusions: Among children with suspected pneumonia, the presence of viral features alone on CXR is not associated with increased rates of antibiotic use. Among children with radiographic pneumonia, the addition of viral features on CXR is associated with lower rates of antibiotic use, as compared to children with radiographic pneumonia alone., (© The Author(s) 2022. Published by Oxford University Press on behalf of The Journal of the Pediatric Infectious Diseases Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Only few studies have focused on differentiating focal pneumonia-like lung cancer (F-PLC) from focal pulmonary inflammatory lesion (F-PIL). This exploratory study aimed to evaluate the clinical value of a combined model incorporating computed tomography (CT)-based radiomics signatures, clinical factors, and CT morphological features for distinguishing F-PLC and F-PIL., Methods: In total, 396 patients pathologically diagnosed with F-PLC and F-PIL from two medical institutions between January 2015 and May 2021 were retrospectively analyzed. Patients from center 1 were included in the training (n = 242) and internal validation (n = 104) cohorts. Moreover, patients from center 2 were classified under the external validation cohort (n = 50). The clinical and CT morphological characteristics of both groups were compared first. And then, a clinical model incorporating clinical and CT morphological features, a radiomics model reflecting the radiomics signature of lung lesions, and a combined model were developed and validated, respectively., Results: Age, gender, smoking history, respiratory symptoms, air bronchogram, necrosis, and pleural attachment differed significantly between the F-PLC and F-PIL groups (all P < 0.05). For the clinical model, age, necrosis, and pleural attachment were the most effective factors to differentiate F-PIL from F-PLC, with the area under the curves (AUCs) of 0.838, 0.819, and 0.717 in the training and internal and external validation cohorts, respectively. For the radiomics model, five radiomics features were found to be significantly related to the identification of F-PLC and F-PIL (all P < 0.001), with the AUCs of 0.804, 0.877, and 0.734 in the training and internal and external validation cohorts, respectively. For the combined model, five radiomics features, age, necrosis, and pleural attachment were independent predictors for distinguishing between F-PLC and F-PIL, with the AUCs of 0.915, 0.899, and 0.805 in the training and internal and external validation cohorts, respectively. The combined model exhibited a better performance than had the clinical and radiomics models., Conclusions: The combined model, which incorporates CT-based radiomics signatures, clinical factors, and CT morphological characteristics, is effective in differentiating F-PLC from F-PIL., (© 2022. The Author(s).)

Although lung involvement in aspiration pneumonia typically has a gravity-dependent distribution on chest images, which patient's conditions contribute to its radiological pattern has not been fully elucidated. This study was designed to determine the factors associated with the gravity-dependent distribution of community-acquired pneumonia (CAP) on chest computed tomography (CT). This retrospective study included elderly patients aged ≥ 65 years with CAP who underwent chest CT within 1 week before or after admission. The factors associated with lower lobe- and posterior-predominant distributions of ground glass opacity or airspace consolidation were determined. Of the 369 patients with CAP, 348 (94%) underwent chest CT. Multivariate analyses showed that impaired consciousness, a low Barthel index of activities of daily living, and high hemoglobin levels were associated with lower lobe-predominant distribution, while male sex and impaired consciousness were associated with posterior-predominant distribution. Cerebrovascular diseases were unrelated to these distributions. While male sex, impaired consciousness, high hemoglobin levels, low albumin levels, and the number of involved lobes were associated with in-hospital mortality, gravity-dependent distributions were not. Impaired consciousness might be the most significant predictor of aspiration pneumonia; however, the gravity-dependent distribution of this disease is unlikely to affect disease prognosis., (© 2022. The Author(s).)

Introduction: Pneumonia is a leading cause of mortality and a common indication for antibiotic in elderly patients. However, its diagnosis is often inaccurate. We aim to compare the diagnostic accuracy, the clinical and cost outcomes and the use of antibiotics associated with three imaging strategies in patients >65 years old with suspected pneumonia in the emergency room (ER): chest X-ray (CXR, standard of care), low-dose CT scan (LDCT) or lung ultrasonography (LUS)., Methods and Analysis: This is a multicentre randomised superiority clinical trial with three parallel arms. Patients will be allocated in the ER to a diagnostic strategy based on either CXR, LDCT or LUS. All three imaging modalities will be performed but the results of two of them will be masked during 5 days to the patients, the physicians in charge of the patients and the investigators according to random allocation. The primary objective is to compare the accuracy of LDCT versus CXR-based strategies. As secondary objectives, antibiotics prescription, clinical and cost outcomes will be compared, and the same analyses repeated to compare the LUS and CXR strategies. The reference diagnosis will be established a posteriori by a panel of experts. Based on a previous study, we expect an improvement of 16% of the accuracy of pneumonia diagnosis using LDCT instead of CXR. Under this assumption, and accounting for 10% of drop-out, the enrolment of 495 patients is needed to prove the superiority of LDCT over CRX (alpha error=0.05, beta error=0.10)., Ethics and Dissemination: Ethical approval: CER Geneva 2019-01288., Trial Registration Number: NCT04978116., Competing Interests: Competing interests: WH has received research funding from the European Union, the Swiss National Science foundation, Zoll foundation, Dräger Medical Germany, Mundipharma Research UK, MDI International Australia, Roche Diagnostics Germany, all outside the submitted work. WH has provided paid consultancies to AO foundation Switzerland and MDI International Australia, all outside the submitted work. WH has received financial support for a congress he chaired from EBSCO Germany, Isabel Healthcare UK, Mundipharma Medical Switzerland, VisualDx USA, all outside the submitted work., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

While high-resolution computed tomography (HRCT) is increasingly performed, its role in diagnosing pulmonary tuberculosis (TB) among elderly patients with community-acquired pneumonia (CAP) has not been fully elucidated. This study aimed to determine HRCT features that can differentiate pulmonary TB from non-TB CAP in elderly patients. This study included consecutive elderly patients (age > 65 years) admitted to two teaching hospitals for pulmonary TB or non-TB pneumonia who met the CAP criteria of the American Thoracic Society/Infectious Diseases Society of America guidelines. After propensity score matching for clinical background between patients with pulmonary TB and those with non-TB CAP, their HRCT features were compared. This study included 151 patients with pulmonary TB and 238 patients with non-TB CAP. The presence of centrilobular nodules, air bronchograms, and cavities and the absence of ground-glass opacities and bronchial wall thickening were significantly associated with pulmonary TB. The negative predictive values of centrilobular nodules, air bronchograms, and cavities for pulmonary TB were moderate (70.6%, 67.9%, and 63.0%, respectively), whereas the positive predictive value of cavities was high (96.6%). In elderly patients, although some HRCT features could differentiate pulmonary TB from non-TB CAP, no useful findings could rule out pulmonary TB with certainty., (© 2022. The Author(s).)

Fungal pneumonia is the most frequent presentation of invasive fungal infections (IFIs) in patients with hematologic malignancies (HM) and hematopoietic stem cell transplantation (HSCT) recipients. The most common causes include Aspergillus, Mucor, Fusarium, and Candida species. The high incidence and high morbidity and mortality rate of fungal pneumonias in HM/HSCT populations arise from severe immune dysfunction that may be caused by both the underlying disease and/or its treatment. CT is routinely used when pulmonary complications are suspected after HSCT. Appropriate image interpretation of the posttransplant patient requires a combination of pattern recognition and knowledge of the clinical setting. In this article, we provide an overview of the clinical manifestations and CT imaging features of the most common invasive fungal pneumonias (IFPs) seen in severely immunosuppressed hosts., (Copyright © 2022 Elsevier Inc. All rights reserved.)

The chest radiograph is the most common imaging examination performed in most radiology departments, and one of the more common indications for these studies is suspected infection. Radiologists must therefore be aware of less common radiographic patterns of pulmonary infection if they are to add value in the interpretation of chest radiographs for this indication. This review uses a case-based format to illustrate a range of imaging findings that can be associated with acute pulmonary infection and highlight findings that should prompt investigation for diseases other than community-acquired pneumonia to prevent misdiagnosis and delays in appropriate management., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Purpose: Patients with pneumonia often present to the emergency department (ED) and require prompt diagnosis and treatment. Clinical decision support systems for the diagnosis and management of pneumonia are commonly utilized in EDs to improve patient care. The purpose of this study is to investigate whether a deep learning model for detecting radiographic pneumonia and pleural effusions can improve functionality of a clinical decision support system (CDSS) for pneumonia management (ePNa) operating in 20 EDs., Materials and Methods: In this retrospective cohort study, a dataset of 7434 prior chest radiographic studies from 6551 ED patients was used to develop and validate a deep learning model to identify radiographic pneumonia, pleural effusions, and evidence of multilobar pneumonia. Model performance was evaluated against 3 radiologists' adjudicated interpretation and compared with performance of the natural language processing of radiology reports used by ePNa., Results: The deep learning model achieved an area under the receiver operating characteristic curve of 0.833 (95% confidence interval [CI]: 0.795, 0.868) for detecting radiographic pneumonia, 0.939 (95% CI: 0.911, 0.962) for detecting pleural effusions and 0.847 (95% CI: 0.800, 0.890) for identifying multilobar pneumonia. On all 3 tasks, the model achieved higher agreement with the adjudicated radiologist interpretation compared with ePNa., Conclusions: A deep learning model demonstrated higher agreement with radiologists than the ePNa CDSS in detecting radiographic pneumonia and related findings. Incorporating deep learning models into pneumonia CDSS could enhance diagnostic performance and improve pneumonia management., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: We investigated whether lung ultrasound (US) performed in primary care is useful and feasible for diagnosing community-acquired pneumonia (CAP) compared with chest radiography, as most previous research has been conducted in hospital settings., Methods: We undertook a prospective observational cohort study of lung US performed in 12 primary care centers. Patients aged 5 years and older with symptoms suggesting CAP were examined with lung US (by 21 family physicians and 7 primary care pediatricians) and chest radiograph on the same day. We compared lung US findings with the radiologist's chest radiograph report as the reference standard, given that the latter is the most common imaging test performed for suspected CAP in primary care. The physicians had varied previous US experience, but all received a 5-hour lung US training program., Results: The study included 82 patients. Compared with chest radiography, positive lung US findings (consolidation measuring >1 cm or a focal/asymmetrical B-lines pattern) showed a sensitivity of 87.8%, a specificity of 58.5%, a positive likelihood-ratio of 2.12, and a negative likelihood-ratio of 0.21. Findings were similar regardless of the physicians' previous US training or experience. We propose a practical algorithm whereby patients having consolidation measuring greater than 1 cm or normal findings on lung US could skip chest radiography, whereas patients with a B-lines pattern without consolidation (given its low specificity) would need chest radiography to ensure appropriate management. Lung US was generally performed in 10 minutes or less., Conclusion: Point-of-care lung US in primary care could be useful for investigating suspected CAP (avoiding chest radiography in most cases) and is likely feasible in daily practice, as short training programs appear sufficient and little time is needed to perform the scan., (© 2022 Annals of Family Medicine, Inc.)

Objectives: To determine the diagnostic accuracy of extended lung ultrasonographic assessment, including evaluation of dynamic air bronchograms and color Doppler imaging to differentiate pneumonia and atelectasis in patients with consolidation on chest radiograph. Compare this approach to the Simplified Clinical Pulmonary Infection Score, Lung Ultrasound Clinical Pulmonary Infection Score, and the Bedside Lung Ultrasound in Emergency protocol., Design: Prospective diagnostic accuracy study., Setting: Adult ICU applying selective digestive decontamination., Patients: Adult patients that underwent a chest radiograph for any indication at any time during admission. Patients with acute respiratory distress syndrome, coronavirus disease 2019, severe thoracic trauma, and infectious isolation measures were excluded., Interventions: None., Measurements and Main Results: Lung ultrasound was performed within 24 hours of chest radiograph. Consolidated tissue was assessed for presence of dynamic air bronchograms and with color Doppler imaging for presence of flow. Clinical data were recorded after ultrasonographic assessment. The primary outcome was diagnostic accuracy of dynamic air bronchogram and color Doppler imaging alone and within a decision tree to differentiate pneumonia from atelectasis. Of 120 patients included, 51 (42.5%) were diagnosed with pneumonia. The dynamic air bronchogram had a 45% (95% CI, 31-60%) sensitivity and 99% (95% CI, 92-100%) specificity. Color Doppler imaging had a 90% (95% CI, 79-97%) sensitivity and 68% (95% CI, 56-79%) specificity. The combined decision tree had an 86% (95% CI, 74-94%) sensitivity and an 86% (95% CI, 75-93%) specificity. The Bedside Lung Ultrasound in Emergency protocol had a 100% (95% CI, 93-100%) sensitivity and 0% (95% CI, 0-5%) specificity, while the Simplified Clinical Pulmonary Infection Score and Lung Ultrasound Clinical Pulmonary Infection Score had a 41% (95% CI, 28-56%) sensitivity, 84% (95% CI, 73-92%) specificity and 68% (95% CI, 54-81%) sensitivity, 81% (95% CI, 70-90%) specificity, respectively., Conclusions: In critically ill patients with pulmonary consolidation on chest radiograph, an extended lung ultrasound protocol is an accurate and directly bedside available tool to differentiate pneumonia from atelectasis. It outperforms standard lung ultrasound and clinical scores., Competing Interests: Drs. Jonkman and Heunks received funding from Liberate Medical. Dr. Heunks also received funding from Getinge Critical Care and Fisher and Paykel. Dr. Tuinman disclosed departmental work. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2022 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

This article reviews the various nonimaging diagnostic tests available for the diagnosis of pneumonia including the methods by which specimens are obtained and the speed at which certain tests are available. Because tests results may be available at the time of radiologic image interpretation, it is important for the radiologist to be aware of the type of specimens and diagnostic tests to search for in the electronic medical record in order to provide a more refined imaging report. Diagnostic tests for the most common bacterial, fungal, viral, and parasitic infections are discussed., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Introduction: Pneumonia is caused by microbes that establish an infectious process in the lungs. The gold standard for pneumonia diagnosis is radiologist-documented pneumonia-related features in radiology notes that are captured in electronic health records in an unstructured format., Objective: The study objective was to develop a methodological approach for assessing validity of a pneumonia diagnosis based on identifying presence or absence of key radiographic features in radiology reports with subsequent rendering of diagnostic decisions into a structured format., Methods: A pneumonia-specific natural language processing (NLP) pipeline was strategically developed applying Clinical Text Analysis and Knowledge Extraction System (cTAKES) to validate pneumonia diagnoses following development of a pneumonia feature-specific lexicon. Radiographic reports of study-eligible subjects identified by International Classification of Diseases (ICD) codes were parsed through the NLP pipeline. Classification rules were developed to assign each pneumonia episode into one of three categories: "positive," "negative," or "not classified: requires manual review" based on tagged concepts that support or refute diagnostic codes., Results: A total of 91,998 pneumonia episodes diagnosed in 65,904 patients were retrieved retrospectively. Approximately 89% (81,707/91,998) of the total pneumonia episodes were documented by 225,893 chest X-ray reports. NLP classified and validated 33% (26,800/81,707) of pneumonia episodes classified as "Pneumonia-positive," 19% as (15401/81,707) as "Pneumonia-negative," and 48% (39,209/81,707) as "episode classification pending further manual review." NLP pipeline performance metrics included accuracy (76.3%), sensitivity (88%), and specificity (75%)., Conclusion: The pneumonia-specific NLP pipeline exhibited good performance comparable to other pneumonia-specific NLP systems developed to date., Competing Interests: None declared., (Thieme. All rights reserved.)

Background: Community-acquired pneumonia (CAP) causes high morbidity and mortality in all age groups worldwide. Lower muscle radiodensity was associated with worse clinical outcomes (including shock) and higher in-hospital mortality. Prompt detection of sarcopenia in older adults with CAP is important. The measurement of muscle mass often involves specialized and expensive techniques. A relatively simple and inexpensive method such as the sarcopenia index (SI) to measure muscle mass would be helpful. Therefore, we performed a retrospective cohort study to assess the association between SI and septic shock risk and mortality in older patients with CAP., Study Design: In this retrospective cohort study, information on hospitalized CAP patients, including general information and septic shock, were obtained from the medical record database of the Southwest Medical University Zigong Affiliated Hospital, China. Data on patient survival and mortality (all-cause) were acquired from government authorities and telephonic follow-up. Serum creatinine (Cr) and cystatin-C (CysC) levels on admission were included in the database. The SI was determined as the serum Cr/CysC ratio × 100 and the participants were assigned to low and high SI groups. The association between SI and septic shock was evaluated by logistic regression, and that between SI and mortality by Cox regression analysis., Results: In total, 769 older adults (≥ 60 years) with CAP were included, of which 480(62.4%) were male and 289(37.6%)were female. We found that the total prevalence of septic shock in older adults with CAP was 16.0%. In the female group, septic shock was more prevalent in the low SI group than in the high SI group (low SI vs. high SI, 22.22% vs. 11.52%, p = 0.024). Following adjustment for confounders, there was a significant association between high SI and a lower risk of septic shock in female patients (OR = 0.38, 95%CI: 0.16-0.94; p < 0.05). The total death toll of older adults with CAP was 332(43.2%). Irrespective of sex, there was a higher risk of mortality in the low SI group (total group: low SI vs. high SI, 63.02% vs. 36.57%, p < 0.001; male group: low SI vs. high SI, 63.03% vs. 39.34%, p < 0.001; female group: low SI vs. high SI, 73.61% vs. 28.57%, p < 0.001) and, after adjustment for confounding factors and irrespective of sex, high SI was a protective factor for mortality in older adults with CAP (total group: HR = 0.64, 95%CI: 0.48-0.84; p < 0.05; male: HR = 0.69, 95%CI: 0.49-0.97; p < 0.05; female: HR = 0.39, 95%CI: 0.24-0.62; p < 0.05)., Conclusion: While the SI effectively predicts mortality in older adults with CAP, it was only found to be associated with septic shock in older females., (© 2022. The Author(s).)

Pneumonia is one of the main causes of child mortality in the world and has been reported by the World Health Organization (WHO) to be the cause of one-third of child deaths in India. Designing an automated classification system to detect pneumonia has become a worthwhile research topic. Numerous deep learning models have attempted to detect pneumonia by applying convolutional neural networks (CNNs) to X-ray radiographs, as they are essentially images and have achieved great performances. However, they failed to capture higher-order feature information of all objects based on the X-ray images because the topology of the X-ray images' dimensions does not always come with some spatially regular locality properties, which makes defining a spatial kernel filter in X-ray images non-trivial. This paper proposes a principal neighborhood aggregation-based graph convolutional network (PNA-GCN) for pneumonia detection. In PNA-GCN, we propose a new graph-based feature construction utilizing the transfer learning technique to extract features and then construct the graph from images. Then, we propose a graph convolutional network with principal neighborhood aggregation. We integrate multiple aggregation functions in a single layer with degree-scalers to capture more effective information in a single layer to exploit the underlying properties of the graph structure. The experimental results show that PNA-GCN can perform best in the pneumonia detection task on a real-world dataset against the state-of-the-art baseline methods.

Objectives: To investigate the rates of radiographic pneumonia and clinical outcomes of children with suspected pneumonia and subcentimeter, subpleural consolidations on point-of-care lung ultrasound., Study Design: We enrolled a prospective convenience sample of children aged 6 months to 18 years undergoing chest radiography (CXR) for pneumonia evaluation in a single tertiary-care pediatric emergency department. Point-of-care lung ultrasound was performed by an emergency medicine physician with subsequent expert review. We determined rates of radiographic pneumonia and clinical outcomes in the children with subcentimeter, subpleural consolidations, stratified by the presence of larger (>1 cm) sonographic consolidations. The children were followed prospectively for 2 weeks to identify a delayed diagnosis of pneumonia., Results: A total of 188 patients, with a median age of 5.8 years (IQR, 3.5-11.0 years), were evaluated. Of these patients, 62 (33%) had subcentimeter, subpleural consolidations on lung ultrasound, and 23 (37%) also had larger (>1 cm) consolidations. Patients with subcentimeter, subpleural consolidations and larger consolidations had the highest rates of definite radiographic pneumonia (61%), compared with 21% among children with isolated subcentimeter, subpleural consolidations. Overall, 23 children with isolated subcentimeter, subpleural consolidations (59%) had no evidence of pneumonia on CXR. Among 16 children with isolated subcentimeter, subpleural consolidations and not treated with antibiotics, none had a subsequent pneumonia diagnosis within the 2-week follow-up period., Conclusions: Children with subcentimeter, subpleural consolidations often had radiographic pneumonia; however, this occurred most frequently when subcentimeter, subpleural consolidations were identified in combination with larger consolidations. Isolated subcentimeter, subpleural consolidations in the absence of larger consolidations should not be viewed as synonymous with pneumonia; CXR may provide adjunctive information in these cases., (Copyright © 2021 Elsevier Inc. All rights reserved.)