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This work proposes a novel technique called Enhanced JAYA (EJAYA) assisted Q-Learning for the classification of pulmonary diseases, such as pneumonia and tuberculosis (TB) sub-classes using chest x-ray images. The work introduces Fuzzy lattices formation to handle real time (non-linear and non-stationary) data based feature extraction using Schrödinger equation. Features based adaptive classification is made possible through the Q-learning algorithm wherein optimal Q-values selection is done via EJAYA optimization algorithm. Fuzzy lattice is formed using x-ray image pixels and lattice Kinetic Energy (K.E.) is calculated using the Schrödinger equation. Feature vector lattices having highest K.E. have been used as an input features for the classifier. The classifier has been employed for pneumonia classification (normal, mild and severe) and Tuberculosis detection (presence or absence). A total of 3000 images have been used for pneumonia classification yielding an accuracy, sensitivity, specificity, precision and F-scores of 97.90%, 98.43%, 97.25%, 97.78% and 98.10%, respectively. For Tuberculosis 600 samples have been used. The achived accuracy, sensitivity, specificity, precision and F-score are 95.50%, 96.39%, 94.40% 95.52% and 95.95%, respectively. Computational time are 40.96 and 39.98 s for pneumonia and TB classification. Classifier learning rate (training accuracy) for pneumonia classes (normal, mild and severe) are 97.907%, 95.375% and 96.391%, respectively and for tuberculosis (present and absent) are 96.928% and 95.905%, respectively. The results have been compared with contemporary classification techniques which shows superiority of the proposed approach in terms of accuracy and speed of classification. The technique could serve as a fast and accurate tool for automated pneumonia and tuberculosis classification., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Background/aim: In this study, we introduce an innovative deep-learning model architecture aimed at enhancing the accuracy of detecting and classifying organizing pneumonia (OP), a condition characterized by the presence of Masson bodies within the alveolar spaces due to lung injury. The variable morphology of Masson bodies and their resemblance to adjacent pulmonary structures pose significant diagnostic challenges, necessitating a model capable of discerning subtle textural and structural differences. Our model incorporates a novel architecture that integrates advancements in three key areas: Semantic segmentation, texture analysis, and structural feature recognition., Materials and Methods: We employed a dataset of whole slide imaging from 20 patients, totaling 100 slides of OP, segmented into training, validation, and testing sets to reflect real-world application scenarios. Our approach utilizes a modified multi-head self-attention mechanism combined with ResUNet for semantic segmentation, enhanced by superpixel concepts. This method facilitates the generation of representative token features through iterative super-token blocks, creating high-resolution token maps that leverage local and high-level feature information for improved accuracy., Results: Benefiting from token features and distribution for enhanced texture alignment with fewer false-positives, the super-token transformer (STT) model achieved a mean intersection over union (mIOU) of 72.42%, with a sensitivity of 47.81%, specificity of 99.83%, positive predictive value of 64.03%, and negative predictive value of 99.94%, highlighting superior efficacy in Masson body segmentation in complex cross-tissue analyses., Conclusion: Our team developed an iterative learning model based on the STT approach, emphasizing token features of super token, including texture and distribution, that enable enhanced alignment with the unique textures of Masson bodies to improve sensitivity and mIOU, The development of this STT model presents a significant advancement in the field of medical image analysis for OP that offers a promising avenue for improving diagnostic precision and patient outcomes in pulmonary pathology., (Copyright © 2024, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Pneumonia is the leading cause of death among children around the world. According to WHO, a total of 740,180 lives under the age of five were lost due to pneumonia in 2019. Lung ultrasound (LUS) has been shown to be particularly useful for supporting the diagnosis of pneumonia in children and reducing mortality in resource-limited settings. The wide application of point-of-care ultrasound at the bedside is limited mainly due to a lack of training for data acquisition and interpretation. Artificial Intelligence can serve as a potential tool to automate and improve the LUS data interpretation process, which mainly involves analysis of hyper-echoic horizontal and vertical artifacts, and hypo-echoic small to large consolidations. This paper presents, Fused Lung Ultrasound Encoding-based Transformer (FLUEnT), a novel pediatric LUS video scoring framework for detecting lung consolidations using fused LUS encodings. Frame-level embeddings from a variational autoencoder, features from a spatially attentive ResNet-18, and encoded patient information as metadata combiningly form the fused encodings. These encodings are then passed on to the transformer for binary classification of the presence or absence of consolidations in the video. The video-level analysis using fused encodings resulted in a mean balanced accuracy of 89.3 %, giving an average improvement of 4.7 % points in comparison to when using these encodings individually. In conclusion, outperforming the state-of-the-art models by an average margin of 8 % points, our proposed FLUEnT framework serves as a benchmark for detecting lung consolidations in LUS videos from pediatric pneumonia patients., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Libertario Demi is the co-founder of UltraAI. The rest of authors declare no conflict of interests., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background and Objectives: Severe pneumonia is the leading cause of death among young children worldwide, disproportionately impacting children who lack access to advanced diagnostic imaging. Here our objectives were to develop and test the accuracy of an artificial intelligence algorithm for detecting features of pulmonary consolidation on point-of-care lung ultrasounds among hospitalized children., Methods: This was a prospective, multicenter center study conducted at academic Emergency Department and Pediatric inpatient or intensive care units between 2018-2020. Pediatric participants from 18 months to 17 years old with suspicion of lower respiratory tract infection were enrolled. Bedside lung ultrasounds were performed using a Philips handheld Lumify C5-2 transducer and standardized protocol to collect video loops from twelve lung zones, and lung features at both the video and frame levels annotated. Data from both affected and unaffected lung fields were split at the participant level into training, tuning, and holdout sets used to train, tune hyperparameters, and test an algorithm for detection of consolidation features. Data collected from adults with lower respiratory tract disease were added to enrich the training set. Algorithm performance at the video level to detect consolidation on lung ultrasound was determined using reference standard diagnosis of positive or negative pneumonia derived from clinical data., Results: Data from 107 pediatric participants yielded 117 unique exams and contributed 604 positive and 589 negative videos for consolidation that were utilized for the algorithm development process. Overall accuracy for the model for identification and localization of consolidation was 88.5%, with sensitivity 88%, specificity 89%, positive predictive value 89%, and negative predictive value 87%., Conclusions: Our algorithm demonstrated high accuracy for identification of consolidation features on pediatric chest ultrasound in children with pneumonia. Automated diagnostic support on an ultraportable point-of-care device has important implications for global health, particularly in austere settings., Competing Interests: Dr. Kenton Gregory received compensation as Principal Investigator for a DARPA award to Inventive Government Solutions Inc, previously owned by Intellectual Ventures Laboratory. Dr. Kenton Gregory also received compensation as a Fellow from Intellectual Ventures Laboratory for a portion of this work. This potential conflict for Drs. Kenton and Cynthia Gregory has been reviewed and managed by OHSU. Since the time this research was completed, Global Health Labs Inc. acquired all the assets of Inventive Government Solutions and Intellectual Ventures Laboratory. Drs. Mehanian and Millin, as well as S. Kulhare and M. Horning, received salary support through Global Health Labs Inc., a non-profit organization that may have a commercial interest in the results of this research and technology. Global Health Labs Inc. has been awarded proprietary ownership of the code utilized in the algorithm. These commercial affiliations do not alter our adherence to PLOS ONE policies on sharing data and materials, (Copyright: © 2024 Kessler et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background and Objectives : Recognizing the crucial gaps in our understanding of pediatric pneumonia post-SARS-CoV-2 infection, this study aimed to assess the relationship between Pediatric Pneumonia Ultrasound Scores (PedPne) and inflammatory biomarkers. The primary objective of this study is to evaluate the predictive value of PedPne in comparison with inflammatory biomarkers (IL-6 and dNLR) for the development of pneumonia in pediatric patients following SARS-CoV-2 infection. Materials and Methods : This longitudinal observational study collected data from pediatric patients diagnosed with pneumonia after an acute SARS-CoV2 infection. The study focused on analyzing changes in PedPne scores and inflammatory markers such as IL-6 and dNLR from initial admission to follow-up at 7 days. Statistical analysis involved calculating the sensitivity, specificity, and Area Under the Curve (AUC) for each biomarker, alongside regression analysis to determine their hazard ratios for predicting pneumonia development. Results : The analysis identified significant cutoff values for dNLR at 1.88 (sensitivity 77.0%, specificity 85.7%, AUC 0.802, p < 0.001), IL-6 at 6.1 pg/mL (sensitivity 70.3%, specificity 92.9%, AUC 0.869, p < 0.001), and PedPne score at 3.3 (sensitivity 75.7%, specificity 78.6%, AUC 0.794, p < 0.001). Conversely, NLR showed lower diagnostic performance (AUC 0.485, p = 0.327). Regression analysis further highlighted the strong predictive power of these markers, with IL-6 showing a fourfold increase in pneumonia risk (HR = 4.25, CI: 2.07-9.53, p < 0.001), dNLR indicating more than a twofold increase (HR = 2.53, CI: 1.19-6.97, p = 0.006), and PedPne score associated with more than a doubling of the risk (HR = 2.60, CI: 1.33-5.18, p < 0.001). Conclusions : The study conclusively demonstrated that both PedPne ultrasound scores and specific inflammatory biomarkers such as dNLR and IL-6 are significant predictors of pneumonia development in pediatric patients post-COVID-19 infection. These findings advocate for the integration of these biomarkers in routine clinical assessments to enhance the diagnostic accuracy and management of pneumonia in children following SARS-CoV-2 infection.

Background: Without increasing radiation exposure, ultralow-dose computed tomography (CT) of the chest provides improved diagnostic accuracy of radiological pneumonia diagnosis compared to a chest radiograph. Yet, radiologist resources to rapidly report the chest CTs are limited. This study aimed to assess the diagnostic accuracy of emergency clinicians' assessments of chest ultralow-dose CTs for community-acquired pneumonia using a radiologist's assessments as reference standard., Methods: This was a cross-sectional diagnostic accuracy study. Ten emergency department clinicians (five junior clinicians, five consultants) assessed chest ultralow-dose CTs from acutely hospitalised patients suspected of having community-acquired pneumonia. Before assessments, the clinicians attended a focused training course on assessing ultralow-dose CTs for pneumonia. The reference standard was the assessment by an experienced emergency department radiologist. Primary outcome was the presence or absence of pulmonary opacities consistent with community-acquired pneumonia. Sensitivity, specificity, and predictive values were calculated using generalised estimating equations., Results: All clinicians assessed 128 ultralow-dose CTs. The prevalence of findings consistent with community-acquired pneumonia was 56%. Seventy-eight percent of the clinicians' CT assessments matched the reference assessment. Diagnostic accuracy estimates were: sensitivity = 83% (95%CI: 77-88), specificity = 70% (95%CI: 59-81), positive predictive value = 80% (95%CI: 74-84), negative predictive value = 78% (95%CI: 73-82)., Conclusion: This study found that clinicians could assess chest ultralow-dose CTs for community-acquired pneumonia with high diagnostic accuracy. A higher level of clinical experience was not associated with better diagnostic accuracy., (© 2024. The Author(s).)

Background: Community-Acquired Pneumonia (CAP) remains a significant global health concern, with a subset of cases progressing to Severe Community-Acquired Pneumonia (SCAP). This study aims to develop and validate a CT-based radiomics model for the early detection of SCAP to enable timely intervention and improve patient outcomes., Methods: A retrospective study was conducted on 115 CAP and SCAP patients at Southern Medical University Shunde Hospital from January to December 2021. Using the Pyradiomics package, 107 radiomic features were extracted from CT scans, refined via intra-class and inter-class correlation coefficients, and narrowed down using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. The predictive performance of the radiomics-based model was assessed through receiver operating characteristic (ROC) analysis, employing machine learning classifiers such as k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF), trained and validated on datasets split 7:3, with a training set (n = 80) and a validation set (n = 35)., Results: The radiomics model exhibited robust predictive performance, with the RF classifier achieving superior precision and accuracy compared to LR, SVM, and KNN classifiers. Specifically, the RF classifier demonstrated a precision of 0.977 (training set) and 0.833 (validation set), as well as an accuracy of 0.925 (training set) and 0.857 (validation set), suggesting its superior performance in both metrics. Decision Curve Analysis (DCA) was utilized to evaluate the clinical efficacy of the RF classifier, demonstrating a favorable net benefit within the threshold ranges of 0.1 to 0.8 for the training set and 0.2 to 0.7 for the validation set., Conclusions: The radiomics model developed in this study shows promise for early SCAP detection and can improve clinical decision-making., (© 2024. The Author(s).)

Colony-stimulating factor 1 receptor (CSF1R) is a type III receptor tyrosine kinase that is crucial for immune cell activation, survival, proliferation, and differentiation. Its expression significantly increases in macrophages during inflammation, playing a crucial role in regulating inflammation resolution and termination. Consequently, CSF1R has emerged as a critical target for both therapeutic intervention and imaging of inflammatory diseases. Herein, we have developed a radiotracer, 1-[4-((7-(dimethylamino)quinazolin-4-yl)oxy)phenyl]-3-(4-[ 18 F]fluorophenyl)urea ([ 18 F] 17 ), for in vivo positron emission tomography (PET) imaging of CSF1R. Compound 17 exhibits a comparable inhibitory potency against CSF1R as the well-known CSF1R inhibitor PLX647. The radiosynthesis of [ 18 F] 17 was successfully performed by radiofluorination of aryltrimethyltin precursor with a yield of approximately 12% at the end of synthesis, maintaining a purity exceeding 98%. In vivo stability and biodistribution studies demonstrate that [ 18 F] 17 remains >90% intact at 30 min postinjection, with no defluorination observed even at 60 min postinjection. The PET/CT imaging study in lipopolysaccharide-induced pulmonary inflammation mice indicates that [ 18 F] 17 offers a more sensitive characterization of pulmonary inflammation compared to traditional [ 18 F]FDG. Notably, [ 18 F] 17 shows a higher discrepancy in uptake ratio between mice with pulmonary inflammation and the sham group. Furthermore, the variations in [ 18 F] 17 uptake ratio observed on day 7 and day 14 correspond to lung density changes observed in CT imaging. Moreover, the expression levels of CSF1R on day 7 and day 14 follow a trend similar to the uptake pattern of [ 18 F] 17 , indicating its potential for accurately characterizing CSF1R expression levels and effectively monitoring the pulmonary inflammation progression. These results strongly suggest that [ 18 F] 17 has promising prospects as a CSF1R PET tracer, providing diagnostic opportunities for pulmonary inflammatory diseases.

Purpose: To compare the diagnostic performance of a thick-slab reconstruction obtained from an ultra-low-dose CT (termed thoracic tomogram) with standard-of-care low-dose CT (SOC-CT) for rapid interpretation and detection of pneumonia in hemato-oncology patients., Methods: Hemato-oncology patients with a working diagnosis of pneumonia underwent an SOC-CT followed by an ultra-low-dose CT, from which the thoracic tomogram (TT) was reconstructed. Three radiologists evaluated the TT and SOC-CT in the following categories: (I) infectious/inflammatory opacities, (II) small airways infectious/inflammatory changes, (III) atelectasis, (IV) pleural effusions, and (V) interstitial abnormalities. The TT interpretation time and radiation dose were recorded. Sensitivity, specificity, diagnostic accuracy, ROC, and AUC were calculated with the corresponding power analyses. The agreement between TT and SOC-CT was calculated by Correlation Coefficient for Repeated Measures (CCRM), and the Shrout-Fleiss intra-class correlations test was used to calculate interrater agreement., Results: Forty-seven patients (mean age 58.7 ± 14.9 years; 29 male) were prospectively enrolled. Sensitivity, specificity, accuracy, AUC, and Power for categories I/II/III/IV/V were: 94.9/99/97.9/0.971/100, 78/91.2/86.5/0.906/100, 88.6/100/97.2/0.941/100, 100/99.2/99.3/0.995/100, and 47.6/100/92.2/0.746/87.3. CCRM between TT and SOC-CT for the same categories were .97/.81/.92/.96/.62 with an interobserver agreement of .93/.88/.82/.96/.61. Mean interpretation time was 18.6 ± 5.4 seconds. The average effective radiation dose of TT was similar to a frontal and lateral chest X-ray (0.27 ± 0.08 vs 1.46 ± 0.64 mSv for SOC-CT; P  < .01)., Conclusion: Thoracic tomograms provide comparable diagnostic information to SOC-CT for the detection of pneumonia in immunocompromised patients at one-fifth of the radiation dose with high interobserver agreement., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest concerning the research, authorship, and/or publication of this article.

To create robust and adaptable methods for lung pneumonia diagnosis and the assessment of its severity using chest X-rays (CXR), access to well-curated, extensive datasets is crucial. Many current severity quantification approaches require resource-intensive training for optimal results. Healthcare practitioners require efficient computational tools to swiftly identify COVID-19 cases and predict the severity of the condition. In this research, we introduce a novel image augmentation scheme as well as a neural network model founded on Vision Transformers (ViT) with a small number of trainable parameters for quantifying COVID-19 severity and other lung diseases. Our method, named Vision Transformer Regressor Infection Prediction (ViTReg-IP), leverages a ViT architecture and a regression head. To assess the model's adaptability, we evaluate its performance on diverse chest radiograph datasets from various open sources. We conduct a comparative analysis against several competing deep learning methods. Our results achieved a minimum Mean Absolute Error (MAE) of 0.569 and 0.512 and a maximum Pearson Correlation Coefficient (PC) of 0.923 and 0.855 for the geographic extent score and the lung opacity score, respectively, when the CXRs from the RALO dataset were used in training. The experimental results reveal that our model delivers exceptional performance in severity quantification while maintaining robust generalizability, all with relatively modest computational requirements. The source codes used in our work are publicly available at https://github.com/bouthainas/ViTReg-IP ., (© 2024. The Author(s).)

Objective: This study aims to design, validate and assess the accuracy a deep learning model capable of differentiation Chest X-Rays between pneumonia, acute respiratory distress syndrome (ARDS) and normal lungs., Materials and Methods: A diagnostic performance study was conducted using Chest X-Ray images from adult patients admitted to a medical intensive care unit between January 2003 and November 2014. X-ray images from 15,899 patients were assigned one of three prespecified categories: "ARDS", "Pneumonia", or "Normal"., Results: A two-step convolutional neural network (CNN) pipeline was developed and tested to distinguish between the three patterns with sensitivity ranging from 91.8% to 97.8% and specificity ranging from 96.6% to 98.8%. The CNN model was validated with a sensitivity of 96.3% and specificity of 96.6% using a previous dataset of patients with Acute Lung Injury (ALI)/ARDS., Discussion: The results suggest that a deep learning model based on chest x-ray pattern recognition can be a useful tool in distinguishing patients with ARDS from patients with normal lungs, providing faster results than digital surveillance tools based on text reports., Conclusion: A CNN-based deep learning model showed clinically significant performance, providing potential for faster ARDS identification. Future research should prospectively evaluate these tools in a clinical setting., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background and Objectives: The scarcity of data for training deep learning models in pediatrics has prompted questions about the feasibility of employing CNNs trained with adult images for pediatric populations. In this work, a pneumonia classification CNN was used as an exploratory example to showcase the adaptability and efficacy of such models in pediatric healthcare settings despite the inherent data constraints., Methods: To develop a curated training dataset with reduced biases, 46,947 chest X-ray images from various adult datasets were meticulously selected. Two preprocessing approaches were tried to assess the impact of thoracic segmentation on model attention outside the thoracic area. Evaluation of our approach was carried out on a dataset containing 5,856 chest X-rays of children from 1 to 5 years old., Results: An analysis of attention maps indicated that networks trained with thorax segmentation placed less attention on regions outside the thorax, thus eliminating potential bias. The ensuing network exhibited impressive performance when evaluated on an adult dataset, achieving a pneumonia discrimination AUC of 0.95. When tested on a pediatric dataset, the pneumonia discrimination AUC reached 0.82., Conclusions: The results of this study show that adult-trained CNNs can be effectively applied to pediatric populations. This could potentially shift focus towards validating adult models over pediatric population instead of training new CNNs with limited pediatric data. To ensure the generalizability of deep learning models, it is important to implement techniques aimed at minimizing biases, such as image segmentation or low-quality image exclusion., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Rollan-Martinez-Herrera et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Society is burdened with stroke-associated pneumonia (SAP) after intracerebral haemorrhage (ICH). Cerebral small vessel disease (CSVD) complicates clinical manifestations of stroke. In this study, we redefined the CSVD burden score and incorporated it into a novel radiological-clinical prediction model for SAP., Materials and Methods: A total of 1278 patients admitted to a tertiary hospital between 1 January 2010 and 31 December 2019 were included. The participants were divided into training and testing groups using fivefold cross-validation method. Four models, two traditional statistical models (logistic regression and ISAN) and two machine learning models (random forest and support vector machine), were established and evaluated. The outcomes and baseline characteristics were compared between the SAP and non-SAP groups., Results: Among the of 1278 patients, 281(22.0%) developed SAP after their first ICH. Multivariate analysis revealed that the logistic regression (LR) model was superior in predicting SAP in both the training and testing groups. Independent predictors of SAP after ICH included total CSVD burden score (OR, 1.29; 95% CI, 1.03-1.54), haematoma extension into ventricle (OR, 2.28; 95% CI, 1.87-3.31), haematoma with multilobar involvement (OR, 2.14; 95% CI, 1.44-3.18), transpharyngeal intubation operation (OR, 3.89; 95% CI, 2.7-5.62), admission NIHSS score ≥ 10 (OR, 2.06; 95% CI, 1.42-3.01), male sex (OR, 1.69; 95% CI, 1.16-2.52), and age ≥ 67 (OR, 2.24; 95% CI, 1.56-3.22). The patients in the SAP group had worse outcomes than those in the non-SAP group., Conclusion: This study established a clinically combined imaging model for predicting stroke-associated pneumonia and demonstrated superior performance compared with the existing ISAN model. Given the poor outcomes observed in patients with SAP, the use of individualised predictive nomograms is vital in clinical practice., (© 2024. The Author(s).)

Severe pneumonia results in high morbidity and mortality despite advanced treatments. This study investigates thoracic muscle mass from chest CT scans as a biomarker for predicting clinical outcomes in ICU patients with severe pneumonia. Analyzing electronic medical records and chest CT scans of 778 ICU patients with severe community-acquired pneumonia from January 2016 to December 2021, AI-enhanced 3D segmentation was used to assess thoracic muscle mass. Patients were categorized into clusters based on muscle mass profiles derived from CT scans, and their effects on clinical outcomes such as extubation success and in-hospital mortality were assessed. The study identified three clusters, showing that higher muscle mass (Cluster 1) correlated with lower in-hospital mortality (8% vs. 29% in Cluster 3) and improved clinical outcomes like extubation success. The model integrating muscle mass metrics outperformed conventional scores, with an AUC of 0.844 for predicting extubation success and 0.696 for predicting mortality. These findings highlight the strong predictive capacity of muscle mass evaluation over indices such as APACHE II and SOFA. Using AI to analyze thoracic muscle mass via chest CT provides a promising prognostic approach in severe pneumonia, advocating for its integration into clinical practice for better outcome predictions and personalized patient management., (© 2024. The Author(s).)

During the COVID-19 pandemic, pneumonia was the leading cause of respiratory failure and death. In addition to SARS-COV-2, it can be caused by several other bacterial and viral agents. Even today, variants of SARS-COV-2 are endemic and COVID-19 cases are common in many places. The symptoms of COVID-19 are highly diverse and robust, ranging from invisible to severe respiratory failure. Current detection methods for the disease are time-consuming and expensive with low accuracy and precision. To address such situations, we have designed a framework for COVID-19 and Pneumonia detection using multiple deep learning algorithms further accompanied by a deployment scheme. In this study, we have utilized four prominent deep learning models, which are VGG-19, ResNet-50, Inception V3 and Xception, on two separate datasets of CT scan and X-ray images (COVID/Non-COVID) to identify the best models for the detection of COVID-19. We achieved accuracies ranging from 86% to 99% depending on the model and dataset. To further validate our findings, we have applied the four distinct models on two more supplementary datasets of X-ray images of bacterial pneumonia and viral pneumonia. Additionally, we have implemented a flask app to visualize the outcome of our framework to show the identified COVID and Non-COVID images. The findings of this study will be helpful to develop an AI-driven automated tool for the cost effective and faster detection and better management of COVID-19 patients., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Islam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Community-acquired pneumonia is one of the most lethal infectious diseases, especially for infants and the elderly. Given the variety of causative agents, the accurate early detection of pneumonia is an active research area. To the best of our knowledge, scoping reviews on diagnostic techniques for pneumonia are lacking. In this scoping review, three major electronic databases were searched and the resulting research was screened. We categorized these diagnostic techniques into four classes (i.e., lab-based methods, imaging-based techniques, acoustic-based techniques, and physiological-measurement-based techniques) and summarized their recent applications. Major research has been skewed towards imaging-based techniques, especially after COVID-19. Currently, chest X-rays and blood tests are the most common tools in the clinical setting to establish a diagnosis; however, there is a need to look for safe, non-invasive, and more rapid techniques for diagnosis. Recently, some non-invasive techniques based on wearable sensors achieved reasonable diagnostic accuracy that could open a new chapter for future applications. Consequently, further research and technology development are still needed for pneumonia diagnosis using non-invasive physiological parameters to attain a better point of care for pneumonia patients.

Inhalation of crack and freebase results in alveolar hemorrhage. In severe courses of the disease, progressive respiratory insufficiency may lead to respiratory failure and acute respiratory distress syndrome (ARDS). Computed tomography of the thorax reveals bilateral consolidation and ground-glass pattern leaving a subpleural gap. This case report of a 48-year-old male patient highlights the importance of a thorough medical history while ruling out infectious causes., (© 2024. The Author(s).)

Pneumonia is one of the largest causes of death in the world. Deep learning techniques can assist doctors to detect the areas of pneumonia in the chest X-rays images. However, existing methods lack sufficient consideration for the large variation scale and the blurred boundary of the pneumonia area. Here, we present a deep learning method based on Retinanet for pneumonia detection. First, we introduce Res2Net into Retinanet to get the multi-scale feature of pneumonia. Then, we proposed a novel predicted boxes fusion algorithm, named Fuzzy Non-Maximum Suppression (FNMS), which gets a more robust predicted box by fusing the overlapping detection boxes. Finally, we get the performance outperforms than existing methods by integrating two models with different backbones. We report the experimental result in the single model case and the model ensemble case. In the single model case, RetinaNet with FNMS algorithm and Res2Net backbone is better than RetinaNet and other models. In the model ensemble case, the final score of predicted boxes that fused by the FNMS algorithm is better than NMS, Soft-NMS, and weighted boxes fusion. Experimental results on the pneumonia detection dataset verify the superiority of the FNMS algorithm and the proposed method in the pneumonia detection task.

Pneumonia mainly refers to lung infections caused by pathogens, such as bacteria and viruses. Currently, deep learning methods have been applied to identify pneumonia. However, the traditional deep learning methods for pneumonia identification take less account of the influence of the lung X-ray image background on the model's testing effect, which limits the improvement of the model's accuracy. In this paper, we propose a deep learning method that considers image background factors and analyzes the proposed method with explainable deep learning for explainability. The essential idea is to remove the image background, improve the pneumonia recognition accuracy, and apply the Grad-CAM method to obtain an explainable deep learning model for pneumonia identification. In the proposed approach, (1) preliminary deep learning models for pneumonia X-ray image identification without considering the background are built; (2) deep learning models for pneumonia X-ray image identification with background consideration are built to improve the accuracy of pneumonia identification; (3) Grad-CAM method is employed to analyze the explainability. The proposed approach improves the accuracy of pneumonia identification, and the highest accuracy of VGG16 reaches 95.6%. The proposed approach can be applied to real pneumonia identification for early detection and treatment.

This study aimed to develop a new simple and effective prognostic model using artificial intelligence (AI)-based chest radiograph (CXR) results to predict the outcomes of pneumonia. Patients aged > 18 years, admitted the treatment of pneumonia between March 2020 and August 2021 were included. We developed prognostic models, including an AI-based consolidation score in addition to the conventional CURB-65 (confusion, urea, respiratory rate, blood pressure, and age ≥ 65) and pneumonia severity index (PSI) for predicting pneumonia outcomes, defined as 30-day mortality during admission. A total of 489 patients, including 310 and 179 patients in training and test sets, were included. In the training set, the AI-based consolidation score on CXR was a significant variable for predicting the outcome (hazard ratio 1.016, 95% confidence interval [CI] 1.001-1.031). The model that combined CURB-65, initial O 2 requirement, intubation, and the AI-based consolidation score showed a significantly high C-index of 0.692 (95% CI 0.628-0.757) compared to other models. In the test set, this model also demonstrated a significantly high C-index of 0.726 (95% CI 0.644-0.809) compared to the conventional CURB-65 and PSI (p < 0.001 and 0.017, respectively). Therefore, a new prognostic model incorporating AI-based CXR results along with traditional pneumonia severity score could be a simple and useful tool for predicting pneumonia outcomes in clinical practice., (© 2024. The Author(s).)

Precision management of fibrotic lung diseases is challenging due to their diverse clinical trajectories and lack of reliable biomarkers for risk stratification and therapeutic monitoring. Here, we validated the accuracy of CMKLR1 as an imaging biomarker of the lung inflammation-fibrosis axis. By analyzing single-cell RNA sequencing datasets, we demonstrated CMKLR1 expression as a transient signature of monocyte-derived macrophages (MDMφ) enriched in patients with idiopathic pulmonary fibrosis (IPF). Consistently, we identified MDMφ as the major driver of the uptake of CMKLR1-targeting peptides in a murine model of bleomycin-induced lung fibrosis. Furthermore, CMKLR1-targeted positron emission tomography in the murine model enabled quantification and spatial mapping of inflamed lung regions infiltrated by CMKLR1-expressing macrophages and emerged as a robust predictor of subsequent lung fibrosis. Last, high CMKLR1 expression by bronchoalveolar lavage cells identified an inflammatory endotype of IPF with poor survival. Our investigation supports the potential of CMKLR1 as an imaging biomarker for endotyping and risk stratification of fibrotic lung diseases.

Objective and Results Description: The study objective was to investigate the potential of quantitative measures of pulmonary inflammation by [18 F]Fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) as a surrogate marker of inflammation in COPD. Patients treated with anti-inflammatory Liraglutide were compared to placebo and correlated with inflammatory markers. 27 COPD-patients (14 receiving Liraglutide treatment and 13 receiving placebo) underwent 4D-respiratory-gated FDG-PET/CT before and after treatment. Two raters independently segmented the lungs from CT images and measured activity in whole lung, mean standard uptake values (SUVmean) corrected for lean-body-mass in the phase-matched PET images of the whole segmented lung volume, and total lesion glycolysis (TLG; SUVmean multiplied by volume). Inter-rater reliability was analyzed with Bland-Altman analysis and correlation plots. We found no differences in metabolic activity in the lungs between the two groups as a surrogate of pulmonary inflammation, and no changes in inflammation markers. The purpose of the research and brief summary of main findings. The degree of and changes in pulmonary inflammation in chronic obstructive pulmonary disease (COPD) may be difficult to ascertain. Measuring metabolic activity as a surrogate marker of inflammation by FDG-PET/CT may be useful, but data on its use in COPD including reproducibility is still limited, especially with respiration-gated technique, which should improve quantification in the lungs. We assessed several quantitative measures of metabolic activity and correlated them with inflammation markers, and we assessed reproducibility of the methods. We found no differences in metabolic activity between the two groups (before and after 40 weeks treatment with Liraglutide vs. placebo). Bland-Altman analysis showed good agreement between the two raters., Trial Registration: The study was conducted between February 2018 and March 2020 at the Department of Pulmonary Diseases at Hospital South West Jutland and Lillebaelt Hospital, Denmark, and registered from March 2018 at clinicaltrials.gov with trial registration number NCT03466021., (© 2024. The Author(s).)

Background: Pneumonia is a ubiquitous health condition with severe outcomes. The advancement of ultrasonography techniques allows its application in evaluating pulmonary diseases, providing safer and accessible bedside therapeutic decisions compared to chest X-ray and chest computed tomography (CT) scan. Because of its aforementioned benefits, we aimed to confirm the diagnostic accuracy of lung ultrasound (LUS) for pneumonia in adults., Methods: A systematic literature search was performed of Medline, Cochrane and Crossref, independently by two authors. The selection of studies proceeded based on specific inclusion and exclusion criteria without restrictions to particular study designs, language or publication dates and was followed by data extraction. The gold standard reference in the included studies was chest X-ray/CT scan or both., Results: Twenty-nine (29) studies containing 6702 participants were included in our meta-analysis. Pooled sensitivity, specificity and PPV were 92% (95% CI: 91-93%), 94% (95% CI: 94 to 95%) and 93% (95% CI: 89 to 96%), respectively. Pooled positive and negative likelihood ratios were 16 (95% CI: 14 to 19) and 0.08 (95% CI: 0.07 to 0.09). The area under the ROC curve of LUS was 0. 9712., Conclusions: LUS has high diagnostic accuracy in adult pneumonia. Its contribution could form an optimistic clue in future updates considering this condition.

Pneumonia is a significant cause of morbidity and mortality in the community and hospital settings. Bacterial, viral, mycobacterial, and fungal pathogens are all potential causative agents of pulmonary infection. Chest radiographs and computed tomography are frequently utilized in the assessment of pneumonia. Learning the imaging patterns of different potential organisms allows the radiologist to formulate an appropriate differential diagnosis. An organism-based approach is used to discuss the imaging findings of different etiologies of pulmonary infection., Competing Interests: Disclosure The authors have no relevant disclosures., (Published by Elsevier Inc.)

Background and Aim: This study aimed to elucidate the effectiveness of bedside thoracic ultrasound according to BLUE protocol and to investigate its superiority over other imaging methods in the emergency service., Methods: A total of 120 patients admitted to our institution's emergency care department due to respiratory distress have been enrolled in this prospective research. Thorax USG has been performed in the right and left hemithorax at the points specified in the BLUE protocol for each patient. Pleural sliding motion, A-lines, B-lines, consolidation, effusion, and the presence of barcode signs were evaluated individually. Age, sex, comorbid diseases, other radiological examination findings, laboratory findings, final clinical diagnosis, and hospitalization-discharge status of the patients were recorded., Results: When a correct diagnosis of pneumonia has been analyzed for imaging techniques, the diagnostic rate of chest radiography was 83.3%, CT was 100.0%, and USG was 66.6%. The correct diagnostic rate of chest radiography was 94.5%; CT and USG were 100.0%. The correct diagnosis of pulmonary edema on chest radiography was 94.5%; CT and USG were 100.0%. While the correct diagnosis of pleural effusion on chest radiography and CT was 100.0%, it was 92.3% in USG imaging. Finally, CT and USG imaging performed better than chest radiography in patients with pneumothorax (chest radiography 80.0%, CT and USG 100%)., Conclusion: USG imaging could be preferred in the diagnosis of pneumonia, pulmonary edema, pleural effusion, pneumothorax, pulmonary embolism, and differential diagnosis at the emergency service., (© 2024. The Author(s), under exclusive licence to Royal Academy of Medicine in Ireland.)

Background: Pneumonitis is a well-described, potentially disabling, or fatal adverse effect associated with both immune checkpoint inhibitors (ICI) and thoracic radiotherapy. Accurate differentiation between checkpoint inhibitor pneumonitis (CIP) radiation pneumonitis (RP), and infective pneumonitis (IP) is crucial for swift, appropriate, and tailored management to achieve optimal patient outcomes. However, correct diagnosis is often challenging, owing to overlapping clinical presentations and radiological patterns., Methods: In this multi-centre study of 455 patients, we used machine learning with radiomic features extracted from chest CT imaging to develop and validate five models to distinguish CIP and RP from COVID-19, non-COVID-19 infective pneumonitis, and each other. Model performance was compared to that of two radiologists., Results: Models to distinguish RP from COVID-19, CIP from COVID-19 and CIP from non-COVID-19 IP out-performed radiologists (test set AUCs of 0.92 vs 0.8 and 0.8; 0.68 vs 0.43 and 0.4; 0.71 vs 0.55 and 0.63 respectively). Models to distinguish RP from non-COVID-19 IP and CIP from RP were not superior to radiologists but demonstrated modest performance, with test set AUCs of 0.81 and 0.8 respectively. The CIP vs RP model performed less well on patients with prior exposure to both ICI and radiotherapy (AUC 0.54), though the radiologists also had difficulty distinguishing this test cohort (AUC values 0.6 and 0.6)., Conclusion: Our results demonstrate the potential utility of such tools as a second or concurrent reader to support oncologists, radiologists, and chest physicians in cases of diagnostic uncertainty. Further research is required for patients with exposure to both ICI and thoracic radiotherapy., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [RL is funded by the Royal Marsden and ICR NIHR BRC, Royal Marsden Cancer charity and SBRI (collaborating with QURE.AI). RL’s institution receives compensation for time spent in a secondment role for the NHS England in lung cancer screening and National Institute of Health and Care Research. He has received research funding from CRUK, Innovate UK (co funded by GE Healthcare, Roche and Optellum), SBRI, RM Partners Cancer Alliance and NIHR (coapplicant with Optellum). He has received honoraria from CRUK and undertakes personal private practice. Dr Merina Ahmed declares research funding from BMS and MSD, honoraria from Astra Zeneca and is on the advisory board for BMS and the data monitoring committee for Astra Zeneca. Dr Fiona McDonald is a speaker for and declares advisory board fees from Astra Zeneca. Matthew Blackledge receives funding from Cancer Research UK, paid to his institution. Simon J Doran receives a salary from Cancer Research UK as the manager of the National Cancer Imaging Translational Accelerator (NCITA) Repository Unit. The Repository Unit provides the repository resource used to store the data for this project, together with the curation expertise and charges the PI of the project a fee for its use. Mitchell Chen is funded by the NIHR (Grant No. CL-2021–21-005) and Academy of Medical Sciences (Grant No. SGL026\1024). Sumeet Hindocha is funded by the UKRI CDT in AI for Healthcare https://ai4health.io (Grant No. P/S023283/1), by Imperial College London and by the Royal Marsden & Institute of Cancer Research NIHR Biomedical Research Centre. Benjamin Hunter is funded by Cancer Research UK (grant reference C309/A31316) and Royal Marsden Partners]., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Pneumonia-related hospitalization may be associated with advanced skeletal muscle loss due to aging (i.e., sarcopenia) or chronic illnesses (i.e., cachexia). Early detection of muscle loss may now be feasible using deep-learning algorithms applied on conventional chest CT., Objectives: To implement a fully automated deep-learning algorithm for pectoralis muscle measures from conventional chest CT and investigate longitudinal associations between these measures and incident pneumonia hospitalization according to Chronic Obstructive Pulmonary Disease (COPD) status., Materials and Methods: This analysis from the Multi-Ethnic Study of Atherosclerosis included participants with available chest CT examinations between 2010 and 2012. We implemented pectoralis muscle composition measures from a fully automated deep-learning algorithm (Mask R-CNN, built on the Faster Region Proposal Network (R-) Convolutional Neural Network (CNN) with an extension for mask identification) for two-dimensional segmentation. Associations between CT-derived measures and incident pneumonia hospitalizations were evaluated using Cox proportional hazards models adjusted for multiple confounders which include but are not limited to age, sex, race, smoking, BMI, physical activity, and forced-expiratory-volume-at-1 s-to-functional-vital-capacity ratio. Stratification analyses were conducted based on baseline COPD status., Results: This study included 2595 participants (51% female; median age: 68 (IQR: 61, 76)) CT examinations for whom we implemented deep learning-derived measures for longitudinal analyses. Eighty-six incident pneumonia hospitalizations occurred during a median 6.67-year follow-up. Overall, pectoralis muscle composition measures did not predict incident pneumonia. However, in fully-adjusted models, only among participants with COPD (N = 507), CT measures like extramyocellular fat index (hazard ratio: 1.98, 95% CI: 1.22, 3.21, p value: 0.02), were independently associated with incident pneumonia., Conclusion: Reliable deep learning-derived pectoralis muscle measures could predict incident pneumonia hospitalization only among participants with known COPD., Clinical Relevance Statement: Pectoralis muscle measures obtainable at zero additional cost or radiation exposure from any chest CT may have independent predictive value for clinical outcomes in chronic obstructive pulmonary disease patients., Key Points: •Identification of independent and modifiable risk factors of pneumonia can have important clinical impact on patients with chronic obstructive pulmonary disease. •Opportunistic CT measures of adipose tissue within pectoralis muscles using deep-learning algorithms can be quickly obtainable at zero additional cost or radiation exposure. •Deep learning-derived pectoralis muscle measurements of intermuscular fat and its subcomponents are independently associated with subsequent incident pneumonia hospitalization., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Background: Lung ultrasonography is an emerging tool in diagnosing community-acquired pneumonia (CAP) - a major cause of mortality worldwide. The objective of the study was to determine the diagnostic performance of point-of-care ultrasound (POCUS) of the lung compared to the chest radiograph in the diagnosis of CAP in adults., Methods: Adults ≥ 18 years presenting at the general and medical outpatient clinics, medical and emergency wards with symptoms of suspected CAP were evaluated using a portable ultrasound device and single posteroanterior chest radiograph. Sensitivity, specificity, positive and negative predictive values (PPV and NPV), positive and negative likelihood ratios (LR+ and LR-) with corresponding 95% confidence intervals were computed for the lung ultrasound (LUS) against the chest radiograph as the criterion standard., Results: Out of the 65 patients eventually studied, 50 (76.9%) were diagnosed with pneumonia by chest radiograph. The sensitivity, specificity, PPV, NPV, LR+, LR- and DOR for the LUS against the chest radiograph, respectively, were 96% (95%CI, 86.3% - 99.5%), 93.3% (95%CI, 68.1% - 99.8%), 98.0% (95%CI, 87.8% - 99.7%), 87.5% (64.1% - 96.5%), 14.4 (95%CI, 2.2 - 95.7), 0.04 (95%CI, 0.01 - 0.17) and 336 (28.3 - 3985.0). The overall accuracy was 95.4% (95%CI, 87.1 - 99.0%). The median time to completion of the LUS was 13 minutes., Conclusion: Lung ultrasound at the point of care is a reasonably accurate tool for the diagnosis of CAP in adults presenting with typical features., Competing Interests: The Authors declare that no competing interest exists, (Copyright © 2024 by West African Journal of Medicine.)

Background: In inoperable stage III NSCLC, the standard of care is chemoradiotherapy and adjuvant durvalumab (IO) for 12 months. Pneumonitis is the commonest toxicity leading to discontinuation of IO. A failure to distinguish between expected radiation-induced changes, IO pneumonitis and infection can lead to unnecessary durvalumab discontinuation. We investigated the use of a structured multidisciplinary review of CT-scans, radiation dose distributions and clinical symptoms for the diagnosis of IO pneumonitis., Methods: A retrospective study was conducted at an academic medical center for patients treated for stage III NSCLC with chemoradiotherapy and adjuvant durvalumab between 2018 and 2021. An experienced thoracic radiologist reviewed baseline and follow-up chest CT-scans, systematically scored radiological features suspected for pneumonitis using a published classification system (Veiga C, Radioth Oncol 2018), and had access to screenshots of radiation dose distributions. Next, two experienced thoracic oncologists reviewed each patients' case record, CT-scans and radiation fields. A final consensus diagnosis incorporating views of expert clinicians and the radiologist was made., Results: Among the 45 included patients, 14/45 (31.1%) had a pneumonitis scored in patient records and durvalumab was discontinued in 11/45 cases (24.4%). Review by the radiologist led to a diagnosis of immune-related pneumonitis only in 6/45 patients (13.3%). Review by pulmonary oncologists led to a diagnosis of immune-related pneumonitis in only 4/45 patients (8.9%). In addition a suspicion of an immune-related pneumonitis was rejected in 3 separate patients (6.7%), after the thoracic oncologists had reviewed the patients' radiation fields., Conclusions: In patients treated using the PACIFIC regimen, multidisciplinary assessment of CT-scans, radiation doses and patient symptoms, resulted in fewer diagnoses of immune-related pneumonitis (8.9%). Our study underscores the challenges in accurately diagnosing either IO-related or radiation pneumonitis in patients undergoing adjuvant immunotherapy after chemoradiotherapy and highlights the need for multidisciplinary review in order to avoid inappropriate cessation of adjuvant IO., 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 © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objectives: To investigate a comprehensive segmentation of chest X-ray (CXR) in promoting deep learning-based World Health Organization's (WHO) radiologically confirmed pneumonia diagnosis in children., Methods: A total of 4400 participants between January 2016 and June 2021were identified for a cross-sectional study and divided into primary endpoint pneumonia (PEP), other infiltrates, and normal groups according to WHO's diagnostic criteria. The CXR was divided into six segments of left lung, right lung, mediastinum, diaphragm, ext-left lung, and ext-right lung by adopting the RA-UNet. To demonstrate the benefits of lung field segmentation in pneumonia diagnosis, the segmented images and images that were not segmented, which constituted seven segmentation combinations, were fed into the CBAM-ResNet under a three-category classification comparison. The interpretability of the CBAM-ResNet for pneumonia diagnosis was also performed by adopting a Grad-CAM module., Results: The RA-UNet achieved a high spatial overlap between manual and automatic segmentation (averaged DSC = 0.9639). The CBAM-ResNet when fed with the six segments achieved superior three-category diagnosis performance (accuracy = 0.8243) over other segmentation combinations and deep learning models under comparison, which was increased by around 6% in accuracy, precision, specificity, sensitivity, F1-score, and around 3% in AUC. The Grad-CAM could capture the pneumonia lesions more accurately, generating a more interpretable visualization and enhancing the superiority and reliability of our study in assisting pediatric pneumonia diagnosis., Conclusions: The comprehensive segmentation of CXR could improve deep learning-based pneumonia diagnosis in childhood with a more reasonable WHO's radiological standardized pneumonia classification instead of conventional dichotomous bacterial pneumonia and viral pneumonia., Clinical Relevance Statement: The comprehensive segmentation of chest X-ray improves deep learning-based WHO confirmed pneumonia diagnosis in children, laying a strong foundation for the potential inclusion of computer-aided pediatric CXR readings in precise classification of pneumonia and PCV vaccine trials efficacy in children., Key Points: • The chest X-ray was comprehensively segmented into six anatomical structures of left lung, right lung, mediastinum, diaphragm, ext-left lung, and ext-right lung. • The comprehensive segmentation improved the three-category classification of primary endpoint pneumonia, other infiltrates, and normal with an increase by around 6% in accuracy, precision, specificity, sensitivity, F1-score, and around 3% in AUC. • The comprehensive segmentation gave rise to a more accurate and interpretable visualization results in capturing the pneumonia lesions., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Rationale and Objectives: This study aimed to develop and evaluate a radiomics-based model combined with clinical and qualitative radiological (semantic feature [SF]) features to predict immune checkpoint inhibitor-related pneumonitis (CIP) in patients with non-small cell lung cancer (NSCLC) treated with programmed cell death protein 1 inhibitors., Materials and Methods: This was a multicenter retrospective casecontrol study conducted from January 1, 2018, to December 31, 2022, at three centers. Patients with NSCLC treated with anti-PD1 were enrolled and randomly divided into two groups (7:3): training (n = 95) and validation (n = 39). Logistic regression (LR) and support vector machine (SVM) algorithms were used to transform features into the models., Results: The study comprised 134 participants from three independent centers (male, 114/134, 85%; mean [±standard deviation] age, 63.92 [±7.9] years). The radiomics score (RS) models built based on the LR and SVM algorithms could accurately predict CIP (area under the receiver operating characteristics curve [AUC], 0.860 [0.780, 0.939] and 0.861 [0.781, 0.941], respectively). The AUCs for the RS-clinic-SF combined model were 0.903 (0.839, 0.967) and 0.826 (0.688, 0.964) in the training and validation cohorts, respectively. Decision curve analysis showed that the combined models achieved high clinical net benefit across the majority of the range of reasonable threshold probabilities., Conclusion: This study demonstrated that the combined model constructed by the identified features of RS, clinical features, and SF has the potential to precisely predict CIP. The RS-clinic-SF combined model has the potential to be used more widely as a practical tool for the noninvasive prediction of CIP to support individualized treatment planning., Competing Interests: DECLARATION OF COMPETING INTEREST The authors declare that the research was carried out in the absence of commercial or financial relationships that could be construed as a potential competing interest., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background: Pneumonia is a major public health problem with an impact on morbidity and mortality. Its management still represents a challenge. The aim was to determine whether a new diagnostic algorithm combining lung ultrasound (LUS) and procalcitonin (PCT) improved pneumonia management regarding antibiotic use, radiation exposure, and associated costs, in critically ill pediatric patients with suspected bacterial pneumonia (BP)., Methods: Randomized, blinded, comparative effectiveness clinical trial. Children < 18y with suspected BP admitted to the PICU from September 2017 to December 2019, were included. PCT was determined at admission. Patients were randomized into the experimental group (EG) and control group (CG) if LUS or chest X-ray (CXR) were done as the first image test, respectively. Patients were classified: 1.LUS/CXR not suggestive of BP and PCT < 1 ng/mL, no antibiotics were recommended; 2.LUS/CXR suggestive of BP, regardless of the PCT value, antibiotics were recommended; 3.LUS/CXR not suggestive of BP and PCT > 1 ng/mL, antibiotics were recommended., Results: 194 children were enrolled, 113 (58.2%) females, median age of 134 (IQR 39-554) days. 96 randomized into EG and 98 into CG. 1. In 75/194 patients the image test was not suggestive of BP with PCT < 1 ng/ml; 29/52 in the EG and 11/23 in the CG did not receive antibiotics. 2. In 101 patients, the image was suggestive of BP; 34/34 in the EG and 57/67 in the CG received antibiotics. Statistically significant differences between groups were observed when PCT resulted < 1 ng/ml (p = 0.01). 3. In 18 patients the image test was not suggestive of BP but PCT resulted > 1 ng/ml, all of them received antibiotics. A total of 0.035 mSv radiation/patient was eluded. A reduction of 77% CXR/patient was observed. LUS did not significantly increase costs., Conclusions: Combination of LUS and PCT showed no risk of mistreating BP, avoided radiation and did not increase costs. The algorithm could be a reliable tool for improving pneumonia management., Clinical Trial Registration: NCT04217980., (© 2024. The Author(s).)

Purpose: Uncontrolled intra-alveolar inflammation is a central pathogenic feature, and its severity translates into a valid prognostic indicator of acute lung injury (ALI). Unfortunately, current clinical imaging approaches are unsuitable for visualizing and quantifying intra-alveolar inflammation. This study aimed to construct a small-sized vascular cell adhesion molecule-1 (VCAM-1)-targeted magnetic particle imaging (MPI) nanoprobe (ESPVPN) to visualize and accurately quantify intra-alveolar inflammation at the molecular level., Methods: ESPVPN was engineered by conjugating a peptide (VHPKQHRGGSK(Cy7)GC) onto a polydopamine-functionalized superparamagnetic iron oxide core. The MPI performance, targeting, and biosafety of the ESPVPN were characterized. VCAM-1 expression in HUVECs and mouse models was evaluated by western blot. The degree of inflammation and distribution of VCAM-1 in the lungs were assessed using histopathology. The expression of pro-inflammatory markers and VCAM-1 in lung tissue lysates was measured using ELISA. After intravenous administration of ESPVPN, MPI and CT imaging were used to analyze the distribution of ESPVPN in the lungs of the LPS-induced ALI models., Results: The small-sized (~10 nm) ESPVPN exhibited superior MPI performance compared to commercial MagImaging ® and Vivotrax, and ESPVPN had effective targeting and biosafety. VCAM-1 was highly expressed in LPS-induced ALI mice. VCAM-1 expression was positively correlated with the LPS-induced dose (R = 0.9381). The in vivo MPI signal showed positive correlations with both VCAM-1 expression (R = 0.9186) and representative pro-inflammatory markers (MPO, TNF-α, IL-6, IL-8, and IL-1β, R > 0.7)., Conclusion: ESPVPN effectively targeted inflammatory lungs and combined the advantages of MPI quantitative imaging to visualize and evaluate the degree of ALI inflammation., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: Adenovirus pneumonia is a common cause of community-acquired pneumonia in children and can mimic bacterial pneumonia, but there are few publications on its radiographic features. This study has evaluated the chest radiography findings of community-acquired adenovirus pneumonia in children. The frequency of radiological findings mimicking bacterial pneumonia was investigated. The clinical features of patients with adenovirus pneumonia possessing radiological findings mimicking bacterial pneumonia were also evaluated., Materials and Methods: The chest radiographs of patients diagnosed with adenovirus pneumonia were retrospectively reviewed. The chest radiographs were interpreted independently by a pediatric infectious disease specialist and a pediatric radiologist. Chest radiography findings mimicking bacterial pneumonia (bacterial-like) were specified as consolidation +/- pleural effusion. Other findings on chest radiography or a completely normal chest X-ray were specified as findings that were compatible with "typical viral pneumonia"., Results: A total of 1407 patients were positive for adenovirus with respiratory multiplex PCR. The 219 patients who met the study criteria were included in the study. Chest radiographs were normal in 58 (26.5 %) patients. The chest radiograph findings mimicked bacterial pneumonia in 41 (18.7 %) patients., Conclusion: Adenovirus pneumonia occurs predominantly in children aged five years and younger, as with other viral pneumonias. The radiographic findings in adenovirus pneumonia are predominantly those seen in viral pneumonia. Increasing age and positivity for only adenovirus without other viruses on respiratory multiplex PCR were associated with the chest radiograph being more likely to be "bacterial-like". Adenovirus may lead to lobar/segmental consolidation at a rate that is not very rare., 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 © 2024. Published by Elsevier Inc.)

Objective: To investigate the diagnostic value of lung ultrasound in hospitalized children with community-acquired pneumonia (CAP). Methods: In the cross-sectional study, a total of 422 children with CAP who were hospitalized in the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, from February 2021 to August 2022 and completed lung ultrasound examination within 48 hours after admission were enrolled. The clinical characteristics, lung ultrasound and chest CT were collected. The patients were divided into two groups according to the signs of pneumonia indicated by chest CT, and the signs of lung ultrasound with diagnostic value were screened according to the signs of pneumonia indicated by chest CT by least absolute shrinkage and selection operator (Lasso) regression. According to severity of the disease, the children were divided into the severe group and the mild group, and the differences of lung ultrasound signs between the two groups were compared. Kruskal-Wallis test, Fisher's exact test was selected for comparison between groups. Random forest classifier wes used to evaluate the value of lung ultrasound in the diagnosis of CAP and prediction of severe pneumonia in children. The receiver operating characteristic curve was used to evaluate the prediction effect. Use DeLong test to compare the area under the curve. Results: Among the 422 cases of CAP, there were 258 males and 164 females, and the age of onset was 2.8 (1.3, 4.3) years. The confluent B-line, consolidation and pleural effusion detected by lung ultrasound were 309 cases (73.2%), 232 cases (55.0%) and 16 cases (3.8%), respectively, and the size of consolidation was 3.0 (0, 11.0) mm. One hundred and ten children (26.1%) with CAP completed chest CT. There were 90 cases with signs of pneumonia in chest CT and 20 cases without signs of pneumonia. Lasso was used for feature selection.Lung consolidation ( OR =2.46), bilateral lung consolidation ( OR =1.16) and confluent B-line ( OR =1.34) were the main index. With random forest classifier, the accuracy of models using full variables and Lasso-selected variables were 0.79 (95% CI 0.70-0.86) and 0.79 (95% CI 0.70-0.86), the sensitivity were 0.81 and 0.81, and the specificity were 0.75 and 0.70, and the area under curve were 0.87 (95% CI 0.81-0.94, P <0.001) and 0.84 (95% CI 0.76-0.91, P <0.001), respectively. There were 97 cases in severe group and 325 cases in mild group. Compared with the mild group, the detection rate of consolidation, multiple consolidation, the size of consolidation and the size of consolidation was adjusted by body surface area (consolidation size/body surface area) in severe group were higher (66 cases (68.0%) vs. 166 cases (51.1%), 42 cases (43.3%) vs. 93 cases (28.6%), 8.0 (0, 17.0) vs. 1.0 (0, 9.0) mm, 12.5 (0, 24.6) vs. 2.1 (0, 17.6), χ 2 =8.59, 9.98, Z =14.40, 12.79, all P <0.05). Using lung ultrasound lung consolidation size and consolidation size/body surface area to predict the severe CAP, the optimal cut-off value were 6.7 mm and 10.2, the accuracy was 0.80 (95% CI 0.75-0.83) and 0.89 (95% CI 0.86-0.92), the sensitivity was 0.99 and 0.99, the specificity was 0.14 and 0.56, respectively, and the area under the curve was 0.66 (95% CI 0.60-0.72, P <0.001) and 0.76 (95% CI 0.70-0.83, P <0.001), respectively. The area under the curve of consolidation size/body surface area was higher than that of consolidation size ( Z =5.50, P <0.001). Conclusions: Consolidation and confluent B-line, are important index for lung ultrasound diagnosis of CAP in children. The actual consolidation size adjusted by body surface area is superior to the size of consolidation in predicting severe CAP.

Pneumonia, an inflammatory lung condition primarily triggered by bacteria, viruses, or fungi, presents distinctive challenges in pediatric cases due to the unique characteristics of the respiratory system and the potential for rapid deterioration. Timely diagnosis is crucial, particularly in children under 5, who have immature immune systems, making them more susceptible to pneumonia. While chest X-rays are indispensable for diagnosis, challenges arise from subtle radiographic findings, varied clinical presentations, and the subjectivity of interpretations, especially in pediatric cases. Deep learning, particularly transfer learning, has shown promise in improving pneumonia diagnosis by leveraging large labeled datasets. However, the scarcity of labeled data for pediatric chest X-rays presents a hurdle in effective model training. To address this challenge, we explore the potential of self-supervised learning, focusing on the Masked Autoencoder (MAE). By pretraining the MAE model on adult chest X-ray images and fine-tuning the pretrained model on a pediatric pneumonia chest X-ray dataset, we aim to overcome data scarcity issues and enhance diagnostic accuracy for pediatric pneumonia. The proposed approach demonstrated competitive performance an AUC of 0.996 and an accuracy of 95.89% in distinguishing between normal and pneumonia. Additionally, the approach exhibited high AUC values (normal: 0.997, bacterial pneumonia: 0.983, viral pneumonia: 0.956) and an accuracy of 93.86% in classifying normal, bacterial pneumonia, and viral pneumonia. This study also investigated the impact of different masking ratios during pretraining and explored the labeled data efficiency of the MAE model, presenting enhanced diagnostic capabilities for pediatric pneumonia., (© 2024. The Author(s).)

Background: Thoracic ultrasonography (TUS) is a commonly used tool for on-farm detection of pneumonia in calves. Different scanning methods have been described, but the performance of novice practitioners after training has not been documented., Methods: In this study, 38 practitioners performed quick TUS (qTUS) on 18-23 calves each. Pneumonia was defined as lung consolidation 1 cm or more in depth. Diagnostic parameters (accuracy [Acc], sensitivity [Se] and specificity [Sp]) were compared to those of an experienced operator. Cohen's kappa and Krippendorff's alpha (Kalpha) were determined. The potential effects of training and exam sessions on performance were evaluated., Results: The average relative Se and Sp were 0.66 (standard deviation [SD] = 0.26; minimum [Min.]-Maximum [Max.] = 0-1) and 0.71 (SD = 0.19; Min.-Max. = 0.25-1), respectively. The average relative Acc was 0.73 (SD = 0.11; Min.-Max. = 0.52-0.96). Over all sessions, Cohen's kappa averaged 0.40 (SD = 0.24; Min.-Max. = 0.014-0.90) and Kalpha was 0.24 (95% confidence interval [CI]: 0.20-0.27), indicating 'fair' agreement. Calf age and housing influenced Se and Sp. Supervised practical training improved Se by 17.5% (95% CI: 0.01-0.34)., Limitations: The separate effects of calf age and housing could not be determined., Conclusion: This study showed that qTUS, like any other clinical skill, has a learning curve, and variability in performance can be substantial. Adequate training and certification of one's skill are recommended to assure good diagnostic accuracy., (© 2024 British Veterinary Association.)

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 © 2023 Elsevier Inc. All rights reserved.)

Purpose: Ventilator-associated pneumonia (VAP) is a common healthcare-associated infection in pediatric intensive care unit (PICU), increasing mortality, antibiotics use and duration of ventilation and hospitalization. VAP diagnosis is based on clinical and chest X-ray (CXR) signs defined by the 2018 Center for Disease Control (gold standard). However, CXR induces repetitive patients' irradiation and technical limitations. This study aimed to investigate if lung ultrasound (LUS) can substitute CXR in the VAP diagnosis., Methods: A monocentric and prospective study was conducted in a French tertiary care hospital. Patients under 18-year-old admitted to PICU between November 2018 and July 2020 with invasive mechanical ventilation for more than 48 h were included. The studied LUS signs were consolidations, dynamic air bronchogram, subpleural consolidations (SPC), B-lines, and pleural effusion. The diagnostic values of each sign associated with clinical signs (cCDC) were compared to the gold standard approach. LUS, chest X-ray, and clinical score were performed daily., Results: Fifty-seven patients were included. The median age was 8 [3-34] months. Nineteen (33%) children developed a VAP. In patients with VAP, B-Lines, and consolidations were highly frequent (100 and 68.8%) and, associated with cCDC, were highly sensitive (100 [79-100] % and 88 [62-98] %, respectively) and specific (95.5 [92-98] % and 98 [95-99] %, respectively). Other studied signs, including SPC, showed high specificity (>97%) but low sensibility (<50%)., Conclusion: LUS seems to be a powerful tool for VAP diagnosis in children with a clinical suspicion, efficiently substituting CXR, and limiting children's exposure to ionizing radiations., (© 2023 Wiley Periodicals LLC.)

Background: Lung ultrasound (US), which is radiation-free and cheaper than chest radiography (CXR), may be a useful modality for the diagnosis of pediatric pneumonia, but there are limited data from low- and middle-income countries., Objectives: The aim of this study was to evaluate the diagnostic performance of non-radiologist, physician-performed lung US compared to CXR for pneumonia in children in a resource-constrained, African setting., Materials and Methods: Children under 5 years of age enrolled in a South African birth cohort study, the Drakenstein Child Health Study, who presented with clinically defined pneumonia and had a CXR performed also had a  lung US performed by a study doctor. Each modality was reported by two readers, using standardized methodology. Agreement between modalities, accuracy (sensitivity and specificity) of lung US and inter-rater agreement were assessed. Either consolidation or any abnormality (consolidation or interstitial picture) was considered as endpoints. In the 98 included cases (median age: 7.2 months; 53% male; 69% hospitalized), prevalence was 37% vs. 39% for consolidation and 52% vs. 76% for any abnormality on lung US and CXR, respectively. Agreement between modalities was poor for consolidation (observed agreement=61%, Kappa=0.18, 95% confidence interval [95% CI]: - 0.02 to 0.37) and for any abnormality (observed agreement=56%, Kappa=0.10, 95% CI: - 0.07 to 0.28). Using CXR as the reference standard, sensitivity of lung US was low for consolidation (47%, 95% CI: 31-64%) or any abnormality (5%, 95% CI: 43-67%), while specificity was moderate for consolidation (70%, 95% CI: 57-81%), but lower for any abnormality (58%, 95% CI: 37-78%). Overall inter-observer agreement of CXR was poor (Kappa=0.25, 95% CI: 0.11-0.37) and was significantly lower than the substantial agreement of lung US (Kappa=0.61, 95% CI: 0.50-0.75). Lung US demonstrated better agreement than CXR for all categories of findings, showing a significant difference for consolidation (Kappa=0.72, 95% CI: 0.58-0.86 vs. 0.32, 95% CI: 0.13-0.51)., Conclusion: Lung US identified consolidation with similar frequency to CXR, but there was poor agreement between modalities. The significantly higher inter-observer agreement of LUS compared to CXR supports the utilization of lung US by clinicians in a low-resource setting., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Pneumonia is a disease caused by bacteria, viruses, and fungi that settle in the alveolar sacs of the lungs and can lead to serious health complications in humans. Early detection of pneumonia is necessary for early treatment to manage and cure the disease. Recently, machine learning-based pneumonia detection methods have focused on pneumonia in adults. Machine learning relies on manual feature engineering, whereas deep learning can automatically detect and extract features from data. This study proposes a deep learning feature extraction-based hybrid approach that combines deep learning and machine learning to detect pediatric pneumonia, which is difficult to standardize. The proposed hybrid approach enhances the accuracy of detecting pediatric pneumonia and simplifies the approach by eliminating the requirement for advanced feature extraction. The experiments indicate that the hybrid approach using a Medium Neural Network based on AlexNet feature extraction achieved a 97.9% accuracy rate and 98.0% sensitivity rate. The results show that the proposed approach achieved higher accuracy rates than state-of-the-art approaches., (© 2023. Australasian College of Physical Scientists and Engineers in Medicine.)

Immune checkpoint inhibitors (ICIs) therapy have revolutionized advanced lung cancer care. Interestingly, the host responses for patients received ICIs therapy are distinguishing from those with cytotoxic drugs, showing potential initial transient worsening of disease burden, pseudoprogression and delayed time to treatment response. Thus, a new imaging criterion to evaluate the response for immunotherapy should be developed. ICIs treatment is associated with unique adverse events, including potential life-threatening immune checkpoint inhibitor-related pneumonitis (ICI-pneumonitis) if treated patients are not managed promptly. Currently, the diagnosis and clinical management of ICI-pneumonitis remain challenging. As the clinical manifestation is often nonspecific, computed tomography (CT) scan and X-ray films play important roles in diagnosis and triage. This article reviews the complications of immunotherapy in lung cancer and illustrates various radiologic patterns of ICI-pneumonitis. Additionally, it is tried to differentiate ICI-pneumonitis from other pulmonary pathologies common to lung cancer such as radiation pneumonitis, bacterial pneumonia and coronavirus disease of 2019 (COVID-19) infection in recent months. Maybe it is challenging to distinguish radiologically but clinical presentation may help., (© 2023 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Background: Artificial intelligence (AI) seems promising in diagnosing pneumonia on chest x-rays (CXR), but deep learning (DL) algorithms have primarily been compared with radiologists, whose diagnosis can be not completely accurate. Therefore, we evaluated the accuracy of DL in diagnosing pneumonia on CXR using a more robust reference diagnosis., Methods: We trained a DL convolutional neural network model to diagnose pneumonia and evaluated its accuracy in two prospective pneumonia cohorts including 430 patients, for whom the reference diagnosis was determined a posteriori by a multidisciplinary expert panel using multimodal data. The performance of the DL model was compared with that of senior radiologists and emergency physicians reviewing CXRs and that of radiologists reviewing computed tomography (CT) performed concomitantly., Results: Radiologists and DL showed a similar accuracy on CXR for both cohorts (p ≥ 0.269): cohort 1, radiologist 1 75.5% (95% confidence interval 69.1-80.9), radiologist 2 71.0% (64.4-76.8), DL 71.0% (64.4-76.8); cohort 2, radiologist 70.9% (64.7-76.4), DL 72.6% (66.5-78.0). The accuracy of radiologists and DL was significantly higher (p ≤ 0.022) than that of emergency physicians (cohort 1 64.0% [57.1-70.3], cohort 2 63.0% [55.6-69.0]). Accuracy was significantly higher for CT (cohort 1 79.0% [72.8-84.1], cohort 2 89.6% [84.9-92.9]) than for CXR readers including radiologists, clinicians, and DL (all p-values < 0.001)., Conclusions: When compared with a robust reference diagnosis, the performance of AI models to identify pneumonia on CXRs was inferior than previously reported but similar to that of radiologists and better than that of emergency physicians., Relevance Statement: The clinical relevance of AI models for pneumonia diagnosis may have been overestimated. AI models should be benchmarked against robust reference multimodal diagnosis to avoid overestimating its performance., Trial Registration: NCT02467192 , and NCT01574066 ., Key Point: • We evaluated an openly-access convolutional neural network (CNN) model to diagnose pneumonia on CXRs. • CNN was validated against a strong multimodal reference diagnosis. • In our study, the CNN performance (area under the receiver operating characteristics curve 0.74) was lower than that previously reported when validated against radiologists' diagnosis (0.99 in a recent meta-analysis). • The CNN performance was significantly higher than emergency physicians' (p ≤ 0.022) and comparable to that of board-certified radiologists (p ≥ 0.269)., (© 2024. The Author(s).)