Role of lung ultrasound for the etiological diagnosis of acute lower respiratory tract infection (ALRTI) in children: a prospective study

Buonsenso, Danilo; Musolino, Anna Maria; Ferro, Valentina; Morello, Rosa; Ventola, Chiara; Liotti, Flora Marzia; Sanctis, Rita De; Chiaretti, Antonio; Biasucci, Daniele Guerino; Spanu, Teresa; Sanguinetti, Maurizio; Valentini, Piero

doi:10.1007/s40477-021-00600-z

Cited by 32 publications

(42 citation statements)

References 49 publications

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“…Among studies that evaluated lung ultrasound in relation to LRTIs, there is a great heterogeneity found across individual studies and a reliable reference standard is absent, which make a longitudinal comparison between different studies very difficult. For example, Buonsenso et al [19] reported that consolidation was found among 91% of cases with acute LRTIs, while vertical deep artifacts (numerous B lines) was reported among 72% of cases, and pleural effusion among 28% of cases. However, they did not differentiate between B3 and B7 lines.…”

Section: Discussionmentioning

confidence: 99%

Chest Ultrasound versus Chest X-Ray in Children with Lower Respiratory Tract Infections

Elkhazragy¹,

Fahmy²,

Attaya³

et al. 2021

OJPed

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Section: Discussionmentioning

confidence: 99%

Chest Ultrasound versus Chest X-Ray in Children with Lower Respiratory Tract Infections

Elkhazragy¹,

Fahmy²,

Attaya³

et al. 2021

OJPed

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“…As has happened in our clinical practice, -in some cases (as in the case of mild and early pulmonary edema), the x-ray can be completely negative; -in other cases (such as in the presence of pleural effusion with consensual atelectasis), chest x-ray may be a confounding factor in suspecting an quadro of pleuropneumonia; -and in still other cases (in children who present a febrile clinical picture in the context of a systemic inflammatory disease / sepsis) chest X-ray can once again erroneously suspect a suspected viral infection and / or bacterial of the lower airways as responsible for fever peaks. 3,4,12 All this obviously has implications in clinical practice: using only the chest X-ray in the initial phase of the diagnostic process, there is a risk of making an incorrect diagnosis with the risk of delaying the correct diagnosis on one side and on the other side of administering unnecessary therapies by making inappropriate use especially of antibiotic therapy.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies performed in the pediatric population have shown that LUS may be better than chest radiography in the diagnosis of community-acquired pneumonia [1][2][3] , can define the etiology of pneumonia 3,4 and many studies have described and validated LUS pattern or score (based mainly on vertical artifacts and subpleural consolidations) in neonatal respiratory disorders 5 and bronchiolitis 6 but also the normal LUS pattern in healthy infants. 2 The latter and other categories of patients are united by having in common the presence of sonographic interstitial syndrome (SIS): the presence of multiple or focal, patched or diffuse vertical artifacts (B-lines) fanning out from the lung wall interface.…”

Section: Introduction and Study Aimmentioning

confidence: 99%

Lung Ultrasound to detect cardiopulmonary interactions in acutely ill children

Buonsenso

Ferro

Morello

et al. 2021

Preprint

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Objective and design: Our prospective observational study is the first study that evaluates the LUS findings of cardiopulmonary interactions in acutely ill children with elevated pro-BNP levels,with the aim of establishing the specific LUS pattern in this category of patients without primary lung diseases.Methodology:We prospectively analyzed epidemiological, clinical, laboratory, instrumental and lung ultrasound parameters in acutely ill children aged 1 month to 18 years admitted to the Department of Pediatrics between March 2020 to August 2020.Among the acutely ill patients evaluated, only patients with pro-BNP> 300 pg / ml and who underwent LUS before the start of any treatment were included. They were stratified into three sub-categories based on the diagnosis A) cardiac disease, B) systemic inflammatory disease / sepsis without functional and / or organic alterations of the myocardium and C) systemic inflammatory disease / sepsis and cardiac disease, and were classified into two groups based on the level of pro-BNP.We also enrolled patients belonging to two other categories (patients with primary infectious lung disease and completely healthy patients) analyzing their epidemiological, clinical, laboratory, instrumental parameters and lung ultrasound findings and comparing them with those of acutely ill children.Results and Conclusion: We found that LUS findings in these acutely ill children are different from the ultrasound pattern of other categories of children and in particular 1) children with acute lower respiratory tract infections and 2) healthy infants.The finding in a child of a sonographic interstitial syndrome with multiple, bright, long, separate and non-confluent B-lines / long vertical artefacts deriving from a normal and regular pleural line, in the absence of subpleural consolidations, is strongly predictive of cardiogenic pulmonary edema or pulmonary congestion in the course of systemic inflammatory disease / sepsis.

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“…Lung ultrasound features considered for the aetiological diagnosis of bacterial pneumonia were as follows (Berce et al, 2019;Buonsenso et al, 2021):…”

Section: Lung Scanning Proceduresmentioning

confidence: 99%

“…This avoids unnecessary exposure of children to ionizing radiation (Buonsenso et al, 2019a). Several studies have demonstrated the usefulness of LUS imaging in the diagnosis and follow-up of community-acquired pneumonia (Berce et al, 2019;Musolino et al, 2019;Najgrodzka et al, 2019;Buonsenso et al, 2021) and, in particular, of bronchiolitis (Basile et al, 2015;Di Mauro et al, 2019;Supino et al, 2019;Buonsenso et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Classification of Lung Disease in Children by Using Lung Ultrasound Images and Deep Convolutional Neural Network

et al. 2021

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Bronchiolitis is the most common cause of hospitalization of children in the first year of life and pneumonia is the leading cause of infant mortality worldwide. Lung ultrasound technology (LUS) is a novel imaging diagnostic tool for the early detection of respiratory distress and offers several advantages due to its low-cost, relative safety, portability, and easy repeatability. More precise and efficient diagnostic and therapeutic strategies are needed. Deep-learning-based computer-aided diagnosis (CADx) systems, using chest X-ray images, have recently demonstrated their potential as a screening tool for pulmonary disease (such as COVID-19 pneumonia). We present the first computer-aided diagnostic scheme for LUS images of pulmonary diseases in children. In this study, we trained from scratch four state-of-the-art deep-learning models (VGG19, Xception, Inception-v3 and Inception-ResNet-v2) for detecting children with bronchiolitis and pneumonia. In our experiments we used a data set consisting of 5,907 images from 33 healthy infants, 3,286 images from 22 infants with bronchiolitis, and 4,769 images from 7 children suffering from bacterial pneumonia. Using four-fold cross-validation, we implemented one binary classification (healthy vs. bronchiolitis) and one three-class classification (healthy vs. bronchiolitis vs. bacterial pneumonia) out of three classes. Affine transformations were applied for data augmentation. Hyperparameters were optimized for the learning rate, dropout regularization, batch size, and epoch iteration. The Inception-ResNet-v2 model provides the highest classification performance, when compared with the other models used on test sets: for healthy vs. bronchiolitis, it provides 97.75% accuracy, 97.75% sensitivity, and 97% specificity whereas for healthy vs. bronchiolitis vs. bacterial pneumonia, the Inception-v3 model provides the best results with 91.5% accuracy, 91.5% sensitivity, and 95.86% specificity. We performed a gradient-weighted class activation mapping (Grad-CAM) visualization and the results were qualitatively evaluated by a pediatrician expert in LUS imaging: heatmaps highlight areas containing diagnostic-relevant LUS imaging-artifacts, e.g., A-, B-, pleural-lines, and consolidations. These complex patterns are automatically learnt from the data, thus avoiding hand-crafted features usage. By using LUS imaging, the proposed framework might aid in the development of an accessible and rapid decision support-method for diagnosing pulmonary diseases in children using LUS imaging.

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Role of lung ultrasound for the etiological diagnosis of acute lower respiratory tract infection (ALRTI) in children: a prospective study

Cited by 32 publications

References 49 publications

Chest Ultrasound versus Chest X-Ray in Children with Lower Respiratory Tract Infections

Chest Ultrasound versus Chest X-Ray in Children with Lower Respiratory Tract Infections

Lung Ultrasound to detect cardiopulmonary interactions in acutely ill children

Classification of Lung Disease in Children by Using Lung Ultrasound Images and Deep Convolutional Neural Network

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