Lung Ultrasound Images Research Articles

What’s hot in neonatal respiratory monitoring?

Impact Respiratory monitoring is an essential part of routine clinical care of the newborn. Recent technological developments have improved respiratory monitoring and allowed for a two-way interaction between the monitored parameter and the level of the provided respiratory support. We herein discuss applications of monitoring by neurally adjusted ventilatory assist, closed-loop oxygen control, tidal capnography, lung ultrasound, diaphragmatic electromyography and lung magnetic resonance imaging.

Ультразвуковой мониторинг легких при COVID-19 пневмониях в ОРИТ: межэкспертная повторяемость при выполнении врачами анестезиологами-реаниматологами: ретроспективное двухцентровое когортное исследование

INTRODUCTION: The subjectivity of ultrasound is a well-recognized limitation of the technique. This issue is particularly relevant for specialists with varying levels of experience performing routine ultrasound monitoring of intensive care unit patients with coronavirus (COVID-19) pneumonia. To achieve optimal interrater agreement, it is essential to determine which protocol and lung ultrasound (LUS) scale are most effective. OBJECTIVE: To investigate the interrater agreement of a 16-zone protocol using semiquantitative lung ultrasound (LUS) and the LUS NMHC (National Medical-Surgical Center) scales in intensive care unit patients with COVID-19, with assessments performed by both an expert and a novice specialist. MATERIALS AND METHODS: A retrospective analysis was performed on data from 161 hospitalized patients with confirmed COVID-19 pneumonia who underwent both computed tomography and lung ultrasound within 24 hours of admission. Lung ultrasound images were recorded as cine loops and scored by both an expert and a novice reader. The 16-zone protocol was tested using two types of scales, including the original LUS NMHC, to determine which provided the most reliable agreement between specialists with different levels of experience. RESULTS: The 16-zone protocol demonstrated high interrater agreement regardless of the scale used (LUS or LUS NMHC), with a correlation of scores between operators greater than 0.9 (p > 0.001). This strong correlation (R > 0.9, p > 0.001) was consistent regardless of the time elapsed before the cine loops were reviewed by both experts and novices. CONCLUSIONS: The developed 16-zone protocol with the LUS NMHC score for patients with COVID-19 pneumonia shows high interrater agreement among anesthesiologists and intensivists with varying levels of ultrasound experience.

Predictors of B-line count in hospitalized patients with COVID-19

Abstract Background B-lines on lung ultrasound (LUS) are non-specific signs of increased density within lung tissue, which can be secondary to pulmonary congestion, pneumonia, or fibrosis. LUS is recommended for managing acute heart failure (HF); however, its value in non-HF populations is less clear. Purpose To identify clinical, laboratory, and echocardiographic predictors of B-lines in a population with Coronavirus disease 2019 (COVID-19). Methods In the ECHOVID-19 study, hospitalized non-ICU patients ≥18 years with confirmed laboratory diagnosis of COVID-19 in Eastern Denmark underwent 8-zone LUS and echocardiography. We used trend and regression analyses to identify associations with the number of B-lines and adjusted for potential confounders (age, sex, body mass index, C-reactive protein, and pandemic wave). Results Among 270 (89%) patients with adequate LUS images, the mean age was 69±14 years, 58% were male and 11% had prior HF. Median time from hospital admission to LUS was 4 [2-8] days. In total, 263 (97%) had ≥1 B-line (median B-line number 14 [9-22]) and median left ventricular ejection fraction [LVEF] was 59 [54-63]). In adjusted models, more B-lines were associated with higher levels of C-reactive protein with an incidence rate ratio (IRR) of 16% (95%CI: 8%-24%) per log-unit increase (P<0.001) and higher Early Warning Score (IRR 5% [95%CI: 1%-0%] per point, P=0.02). Higher tricuspid regurgitation gradient was associated with more B-lines: IRR 2% (95%CI: 1%-3%) per mmHg, P<0.001) (Figure 1). Echocardiographic measures of left ventricular (LV) function (including LVEF and global longitudinal strain, left atrial volume, and LV filling pressures (E/e’)) and right ventricular function were not significantly associated with the number of B-lines. B-lines were not associated with N-terminal pro-B-type natriuretic peptide (NT-proBNP) concentrations, even in patients with established HF. Lastly, B-lines were not associated with 30-day mortality. Conclusions Among hospitalized patients with COVID-19, B-lines on LUS are associated with markers of infectious disease severity and pulmonary hypertension but not with markers of left-sided HF.Figure1.Predictors of B-lines

Establishment of seven lung ultrasound phenotypes: a retrospective observational study of an LUS registry

BackgroundLung phenotypes have been extensively utilized to assess lung injury and guide precise treatment. However, current phenotypic evaluation methods rely on CT scans and other techniques. Although lung ultrasound (LUS) is widely employed in critically ill patients, there is a lack of comprehensive and systematic identification of LUS phenotypes based on clinical data and assessment of their clinical value.MethodsOur study was based on a retrospective database. A total of 821 patients were included from September 2019 to October 2020. 1902 LUS examinations were performed in this period. Using a dataset of 55 LUS examinations focused on lung injuries, a group of experts developed an algorithm for classifying LUS phenotypes based on clinical practice, expert experience, and lecture review. This algorithm underwent validation and refinement with an additional 140 LUS images, leading to five iterative revisions and the generation of 1902 distinct LUS phenotypes. Subsequently, a validated machine learning algorithm was applied to these phenotypes. To assess the algorithm’s effectiveness, experts manually verified 30% of the phenotypes, confirming its efficacy. Using K-means cluster analysis and expert image selection from the 1902 LUS examinations, we established seven distinct LUS phenotypes. To further explore the diagnostic value of these phenotypes for clinical diagnosis, we investigated their auxiliary diagnostic capabilities.ResultsA total of 1902 LUS phenotypes were tested by randomly selecting 30% to verify the phenotypic accuracy. With the 1902 LUS phenotypes, seven lung ultrasound phenotypes were established through statistical K-means cluster analysis and expert screening. The acute respiratory distress syndrome (ARDS) exhibited gravity-dependent phenotypes, while the cardiogenic pulmonary edema exhibited nongravity phenotypes. The baseline characteristics of the 821 patients included age (66.14 ± 11.76), sex (560/321), heart rate (96.99 ± 23.75), mean arterial pressure (86.5 ± 13.57), Acute Physiology and Chronic Health Evaluation II (APACHE II)score (20.49 ± 8.60), and duration of ICU stay (24.50 ± 26.22); among the 821 patients, 78.8% were cured. In severe pneumonia patients, the gravity-dependent phenotype accounted for 42% of the cases, whereas the nongravity-dependent phenotype constituted 58%. These findings highlight the value of applying different LUS phenotypes in various diagnoses.ConclusionsSeven sets of LUS phenotypes were established through machine learning analysis of retrospective data; these phenotypes could represent the typical characteristics of patients with different types of critical illness.

Active learning based on multi-enhanced views for classification of multiple patterns in lung ultrasound images

There are several main patterns in lung ultrasound (LUS) images, including A-lines, B-lines, consolidation and pleural effusion. LUS images of healthy lungs typically only exhibit A-lines, while other patterns may emerge or coexist in LUS images associated with different lung diseases. The accurate categorization of these primary patterns is pivotal for effective lung disease screening. However, two challenges complicate the classification task: the first is the inherent blurring of feature differences between main patterns due to ultrasound imaging properties; and the second is the potential coexistence of multiple patterns in a single case, with only the most dominant pattern being clinically annotated. To address these challenges, we propose the active learning based on multi-enhanced views (MEVAL) method to achieve more precise pattern classification in LUS. To accentuate feature differences between multiple patterns, we introduce a feature enhancement module by applying vertical linear fitting and k-means clustering. The multi-enhanced views are then employed in parallel with the original images, thus enhancing MEVAL’s awareness of feature differences between multiple patterns. To tackle the patterns coexistence issue, we propose an active learning strategy based on confidence sets and misclassified sets. This strategy enables the network to simultaneously recognize multiple patterns by selectively labeling of a small number of images. Our dataset comprises 5075 LUS images, with approximately 4% exhibiting multiple patterns. Experimental results showcase the effectiveness of the proposed method in the classification task, with accuracy of 98.72%, AUC of 0.9989, sensitivity of 98.76%, and specificity of 98.16%, which outperforms than the state-of-the-art deep learning-based methods. A series of comprehensive ablation studies suggest the effectiveness of each proposed component and show great potential in clinical application.

Efficacy of Lung Ultrasound vs Chest X-Ray in Detecting Lung Consolidation and Edema in Premature Infants in the NICU.

BACKGROUND The incidence of lung diseases in premature newborns is significantly higher than in full-term newborns due to their underdeveloped lungs. Ultrasound and X-ray are commonly-used bedside examinations in neonatology. This study primarily compares the efficacy of chest X-ray (CXR) and lung ultrasound (LUS) images in evaluating lung consolidation and edema in premature newborns at Neonatal Intensive Care Units (NICU). MATERIAL AND METHODS A retrospective analysis was conducted on LUS and CXR examination results, along with clinical records of premature newborns admitted to our hospital's NICU from November 1, 2019, to December 31, 2021. CXR and LUS scans were performed on the same newborn within a day. We evaluated the consolidations and edema by interpreting the CXR and LUS images, then compared the findings. RESULTS Out of 75 cases, 34 showed lung consolidations on LUS (45%), while only 14 exhibited consolidations on CXR (19%). The detection rate of consolidations by LUS was significantly higher compared to CXR (34/75 vs 14/75, P<0.001). Differences were observed between the 2 bedside examinations in identifying consolidations, with some cases seen only on LUS. CXR struggled to accurately assess the severity of lung edema visible on LUS, showing significant disparity in detecting interstitial edema (53/75 vs 21/75, P<0.001). CONCLUSIONS LUS outperforms chest CXR for bedside assessment of lung consolidation and edema in premature newborns.

Advancing lung ultrasound imaging: advanced pre-processing and neoteric segmentation for improved accuracy

Abstract Within the medical imaging domain, the segmentation of Lung Ultrasound (LUS) images is crucial for accurately defining regions of interest, particularly for lung health assessments. various techniques such as threshold-based, region-based, and edge-based methods have been employed to address this challenge, their accuracy and efficiency are often questioned. This highlights the need for an innovative approach. With this context in mind, this research introduces a novel preprocessing step using Optimized Gaussian Histogram Equalization (GHE) followed by a Neoteric Segmentation (NS) approach. This combination enhances the capability of segmenting specific regions within LUS images, regardless of the overarching medical scenario. The primary focus of this method is to understand, through a thorough evaluation, how neoteric segmentation compares with more conventional methods. By conducting an in-depth comparative analysis, valuable insights can be unearthed. The results demonstrate that the optimized GHE technique significantly enhances contrast and preserves visual quality, while the NS approach shows superior performance in accurately segmenting the lung regions. Initial findings suggest that the NS method, achieving an impressive 95% accuracy, holds significant promise and could influence the trajectory of healthcare practices. This research aims to advance the field of medical image analysis by highlighting the potential benefits of this cutting-edge segmentation technique. These advancements could positively impact healthcare outcomes, especially in contexts that leverage lung ultrasound imaging.

Comparison of lung ultrasound assisted by artificial intelligence to radiology examination in pneumothorax.

Lung ultrasound can evaluate for pneumothorax but the accuracy of diagnosis depends on experience among physicians. This study aimed to investigate the sensitivity and specificity of intelligent lung ultrasound in comparison with chest x-ray, employing chest computed tomography (CT) as the gold standard for diagnosis of pneumothorax in critical ill patients. This prospective, observational study included 75 dyspnea patients admitted to the Intensive Care Unit of the Fourth Affiliated Hospital of Soochow University from January 2021 to April 2023. Lung ultrasound images were collected using BLUE-plus protocol and analyzed by artificial intelligence software to identify the pleural line, with CT results serving as the gold standard for diagnosis. Pneumothorax was diagnosed based on either the disappearance of pleural slip sign or identification of lung point. Additionally, chest x-ray images and diagnostic results were also obtained during the same period for comparison. The sensitivity and specificity of intelligent lung ultrasound in diagnosing pneumothorax were 79.4% and 85.4%, respectively. The sensitivity and specificity of x-ray diagnosis were 82.4% and 80.5%. Additionally, the diagnostic time for lung ultrasound was significantly shorter than that for x-ray examination. Intelligent lung ultrasound has diagnostic efficiency comparable to that of x-ray examination but offers advantages in terms of speed.

Deep learning-based uncertainty estimation for accurate lung ultrasound image analysis

Deep learning-based uncertainty estimation for accurate lung ultrasound image analysis

Personalized mechanical ventilation guided by ultrasound in patients with acute respiratory distress syndrome (PEGASUS): study protocol for an international randomized clinical trial

BackgroundAcute respiratory distress syndrome (ARDS) is a frequent cause of hypoxemic respiratory failure with a mortality rate of approximately 30%. Identifying ARDS subphenotypes based on “focal” or “non-focal” lung morphology has the potential to better target mechanical ventilation strategies of individual patients. However, classifying morphology through chest radiography or computed tomography is either inaccurate or impractical. Lung ultrasound (LUS) is a non-invasive bedside tool that can accurately distinguish “focal” from “non-focal” lung morphology. We hypothesize that LUS-guided personalized mechanical ventilation in ARDS patients leads to a reduction in 90-day mortality compared to conventional mechanical ventilation.MethodsThe Personalized Mechanical Ventilation Guided by UltraSound in Patients with Acute Respiratory Distress Syndrome (PEGASUS) study is an investigator-initiated, international, randomized clinical trial (RCT) that plans to enroll 538 invasively ventilated adult intensive care unit (ICU) patients with moderate to severe ARDS. Eligible patients will receive a LUS exam to classify lung morphology as “focal” or “non-focal”. Thereafter, patients will be randomized within 12 h after ARDS diagnosis to receive standard care or personalized ventilation where the ventilation strategy is adjusted to the morphology subphenotype, i.e., higher positive end-expiratory pressure (PEEP) and recruitment maneuvers for “non-focal” ARDS and lower PEEP and prone positioning for “focal” ARDS. The primary endpoint is all-cause mortality at day 90. Secondary outcomes are mortality at day 28, ventilator-free days at day 28, ICU length of stay, ICU mortality, hospital length of stay, hospital mortality, and number of complications (ventilator-associated pneumonia, pneumothorax, and need for rescue therapy). After a pilot phase of 80 patients, the correct interpretation of LUS images and correct application of the intervention within the safe limits of mechanical ventilation will be evaluated.DiscussionPEGASUS is the first RCT that compares LUS-guided personalized mechanical ventilation with conventional ventilation in invasively ventilated patients with moderate and severe ARDS. If this study demonstrates that personalized ventilation guided by LUS can improve the outcomes of ARDS patients, it has the potential to shift the existing one-size-fits-all ventilation strategy towards a more individualized approach.Trial registrationThe PEGASUS trial was registered before the inclusion of the first patient, https://clinicaltrials.gov/ (ID: NCT05492344).

Combining quantitative and qualitative analysis for scoring pleural line in lung ultrasound

Objective. Accurate assessment of pleural line is crucial for the application of lung ultrasound (LUS) in monitoring lung diseases, thereby aim of this study is to develop a quantitative and qualitative analysis method for pleural line. Approach. The novel cascaded deep learning model based on convolution and multilayer perceptron was proposed to locate and segment the pleural line in LUS images, whose results were applied for quantitative analysis of textural and morphological features, respectively. By using gray-level co-occurrence matrix and self-designed statistical methods, eight textural and three morphological features were generated to characterize the pleural lines. Furthermore, the machine learning-based classifiers were employed to qualitatively evaluate the lesion degree of pleural line in LUS images. Main results. We prospectively evaluated 3770 LUS images acquired from 31 pneumonia patients. Experimental results demonstrated that the proposed pleural line extraction and evaluation methods all have good performance, with dice and accuracy of 0.87 and 94.47%, respectively, and the comparison with previous methods found statistical significance (P < 0.001 for all). Meanwhile, the generalization verification proved the feasibility of the proposed method in multiple data scenarios. Significance. The proposed method has great application potential for assessment of pleural line in LUS images and aiding lung disease diagnosis and treatment.

Competencies for proficiency in basic point-of-care ultrasound in anesthesiology: national expert recommendations using Delphi methodology.

Point-of-care ultrasound (POCUS) allows for rapid bedside assessment and guidance of patient care. Recently, POCUS was included as a mandatory component of Canadian anesthesiology training; however, there is no national consensus regarding the competencies to guide curriculum development. We therefore aimed to define national residency competencies for basic perioperative POCUS proficiency. We adopted a Delphi process to delineate relevant POCUS competencies whereby we circulated an online survey to academic anesthesiologists identified as POCUS leads/experts (n = 25) at all 17 Canadian anesthesiology residency programs. After reviewing a list of competencies derived from the Royal College of Physicians and Surgeons of Canada's National Curriculum, we asked participants to accept, refine, delete, or add competencies. Three rounds were completed between 2022 and 2023. We discarded items with < 50% agreement, revised those with 50-79% agreement based upon feedback provided, and maintained unrevised those items with ≥ 80% agreement. We initially identified and circulated (Round 1) 74 competencies across 19 clinical domains (e.g., basics of ultrasound [equipment, nomenclature, clinical governance, physics]; cardiac [left ventricle, right ventricle, valve assessment, pericardial effusion, intravascular volume status] and lung ultrasound anatomy, image acquisition, and image interpretation; and clinical applications [monitoring and serial assessments, persistent hypotension, respiratory distress, cardiac arrest]). After three Delphi rounds (and 100% response rate maintained), panellists ultimately agreed upon 75 competencies. Through national expert consensus, this study identified POCUS competencies suitable for curriculum development and assessment in perioperative anesthesiology. Next steps include designing and piloting a POCUS curriculum and assessment tool(s) based upon these nationally defined competencies.

Exploring the diagnostic value of ultrasound radiomics for neonatal respiratory distress syndrome

BackgroundNeonatal respiratory distress syndrome (NRDS) is a prevalent cause of respiratory failure and death among newborns, and prompt diagnosis is imperative. Historically, diagnosis of NRDS relied mostly on typical clinical manifestations, chest X-rays, and CT scans. However, recently, ultrasound has emerged as a valuable and preferred tool for aiding NRDS diagnosis. Nevertheless, evaluating lung ultrasound imagery necessitates rigorous training and may be subject to operator-dependent bias, limiting its widespread use. As a result, it is essential to investigate a new, reliable, and operator-independent diagnostic approach that does not require subjective factors or operator expertise. This article aims to explore the diagnostic potential of ultrasound-based radiomics in differentiating NRDS from other non-NRDS lung disease.MethodsA total of 150 neonatal lung disease cases were consecutively collected from the department of neonatal intensive care unit of the Quanzhou Maternity and Children’s Hospital, Fujian Province, from September 2021 to October 2022. Of these patients, 60 were diagnosed with NRDS, whereas 30 were diagnosed with neonatal pneumonia, meconium aspiration syndrome (MAS), and transient tachypnea (TTN). Two ultrasound images with characteristic manifestations of each lung disease were acquired and divided into training (n = 120) and validation cohorts (n = 30) based on the examination date using an 8:2 ratio. The imaging texture features were extracted using PyRadiomics and, after the screening, machine learning models such as random forest (RF), logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and multilayer perceptron (MLP) were developed to construct an imaging-based diagnostic model. The diagnostic efficacy of each model was analyzed. Lastly, we randomly selected 282 lung ultrasound images and evaluated the diagnostic efficacy disparities between the optimal model and doctors across differing levels of expertise.ResultsTwenty-two imaging-based features with the highest weights were selected to construct a predictive model for neonatal respiratory distress syndrome. All models exhibited favorable diagnostic performances. Analysis of the Youden index demonstrated that the RF model had the highest score in both the training (0.99) and validation (0.90) cohorts. Additionally, the calibration curve indicated that the RF model had the best calibration (P = 0.98). When compared to the diagnostic performance of experienced and junior physicians, the RF model had an area under the curve (AUC) of 0.99; however, the values for experienced and junior physicians were 0.98 and 0.85, respectively. The difference in diagnostic efficacy between the RF model and experienced physicians was not statistically significant (P = 0.24), whereas that between the RF model and junior physicians was statistically significant (P < 0.0001).ConclusionThe RF model exhibited excellent diagnostic performance in the analysis of texture features based on ultrasound radiomics for diagnosing NRDS.

Detection of COVID-19 features in lung ultrasound images using deep neural networks

BackgroundDeep neural networks (DNNs) to detect COVID-19 features in lung ultrasound B-mode images have primarily relied on either in vivo or simulated images as training data. However, in vivo images suffer from limited access to required manual labeling of thousands of training image examples, and simulated images can suffer from poor generalizability to in vivo images due to domain differences. We address these limitations and identify the best training strategy.MethodsWe investigated in vivo COVID-19 feature detection with DNNs trained on our carefully simulated datasets (40,000 images), publicly available in vivo datasets (174 images), in vivo datasets curated by our team (958 images), and a combination of simulated and internal or external in vivo datasets. Seven DNN training strategies were tested on in vivo B-mode images from COVID-19 patients.ResultsHere, we show that Dice similarity coefficients (DSCs) between ground truth and DNN predictions are maximized when simulated data are mixed with external in vivo data and tested on internal in vivo data (i.e., 0.482 ± 0.211), compared with using only simulated B-mode image training data (i.e., 0.464 ± 0.230) or only external in vivo B-mode training data (i.e., 0.407 ± 0.177). Additional maximization is achieved when a separate subset of the internal in vivo B-mode images are included in the training dataset, with the greatest maximization of DSC (and minimization of required training time, or epochs) obtained after mixing simulated data with internal and external in vivo data during training, then testing on the held-out subset of the internal in vivo dataset (i.e., 0.735 ± 0.187).ConclusionsDNNs trained with simulated and in vivo data are promising alternatives to training with only real or only simulated data when segmenting in vivo COVID-19 lung ultrasound features.

Lung ultrasound in the evaluation of pulmonary edema in newborns with critical congenital heart disease

BackgroundNewborns with critical congenital heart disease (CCHD) with increased pulmonary blood flow (PBF) are at high risk for congestive heart failure. In this study, we aimed to evaluate the presence and degree of pulmonary edema in newborns with CCHD using lung ultrasound (LUS) during the perioperative period. MethodsProspective clinical trial, 44 newborn patients with CCHD were evaluated in this prospective clinical trial. LUS was repeatedly performed to determine the course of pulmonary edema during the perioperative period. LUS was performed simultaneously with chest radiography (CXR), which was the main part of patient management. The primary outcome of this study was to identify whether a correlation existed between LUS and CXR findings. The secondary outcomes were to determine the relationship between LUS and the need for respiratory support, diuretic use, vasoactive inotropic score (VIS), and pro-B-type natriuretic peptide (pro-BNP) levels during the perioperative period. ResultsThe mean gestational age of the patients was 38.3 ± 1.7 weeks, with a mean birth weight of 3026 ± 432 g. In the preoperative period, both LUS and CXR images were consistent with clinical signs of pulmonary edema. On the first postoperative day, pulmonary edema increased compared to the preoperative period but gradually decreased by the 6th day of surgery (p < 0.05). Positive correlations were observed between the LUS and CXR findings at all study points (p < 0.05). The LUS findings exhibited trends parallel to those of VIS, serum pro-BNP levels, need for respiratory support, and diuretic requirements. As expected, these trends were more pronounced in CCHDs where PBF increased. ConclusionIn CCHD, serial lung ultrasound (LUS) assessments, particularly in cases with increased PBF, can provide valuable guidance for managing patients during the perioperative period.

Differentiating Viral from Bacterial Pneumonia in Children: The Diagnostic Role of Lung Ultrasound-A Prospective Observational Study.

This prospective observational study aimed to investigate the utility of lung ultrasound (LUS) in diagnosing and managing pediatric respiratory infections, specifically focusing on viral, bacterial, and SARS-CoV-2 infections. Conducted over a period of 1 year and 8 months, this research involved 85 pediatric patients (showcasing a median age of 14 months) recruited based on specific criteria, including age, confirmed infection through multiplex PCR tests, and willingness to undergo LUS imaging. This study employed a 12-area scoring system for LUS examinations, utilizing the lung ultrasound score (LUSS) to evaluate lung abnormalities. The PCR examination results reveal diverse respiratory pathogens, with SARS-CoV-2, influenza, and bacterial co-infections being prominent among the cases. As an observational study, this study was not registered in the registry. Distinct LUS patterns associated with different pathogens were identified, showcasing the discriminatory potential of LUS in differentiating between viral and bacterial etiologies. Bacterial infections demonstrated more severe lung involvement, evident in significantly higher LUSS values compared with viral cases (p < 0.0001). The specific abnormalities found in bacterial superinfection can be integrated into diagnostic and management protocols for pediatric respiratory infections. Overall, this research contributes valuable insights into optimizing LUS as a diagnostic tool in pediatric pneumonia, facilitating more informed and tailored healthcare decisions.

Substantiating and Adopting Lung Ultrasound Scores to Predict Surfactant Need in Preterm Neonates with Respiratory Distress Syndrome within an Institution.

Administering surfactant timely and appropriately is important to minimize lung injury but remains challenging in preterm neonates with respiratory distress syndrome. The published literature supports that lung ultrasound (LUS) score can predict surfactant need. Neonatal LUS scanning specification and parameter setting guidelines have been recently published for standardization. However, variations in scanning protocols and machine settings hinder its clinical implementation widely. This observational study aims to internally validate the suggested LUS protocol in a neonatal intensive care unit to establish a correlation between LUS scores and surfactant need as the first step of integrating LUS in the clinical practice. LUS was performed on 40 eligible preterm neonates within 3 hours after birth or before surfactant administration between May 2020 and March 2021. The neonates were between 27 and 32 weeks' gestational age, and all had respiratory distress. Neonates with known congenital anomalies were excluded. A high-frequency linear probe was used to obtain LUS images from six lung zones which were scored using a 0 to 3 system, yielding a maximum of 18 points. Treating physicians were blinded to the LUS score. Receiver operating characteristic analysis determined the optimal LUS score cut-off for predicting surfactant need. Fifteen of the 40 neonates (38%) required higher oxygen fraction and received surfactant. In our cohort, an LUS score ≥10 was identified as the optimal cut-off for predicting surfactant need, with a sensitivity of 80% and specificity of 84%. The area under the curve was 0.8 (p = 0.0003). LUS predicted surfactant need at a median of 3.5 hours earlier than traditional clinical decision (p < 0.0037). LUS is a helpful adjunct for predicting surfactant need in preterm neonates. This study describes an approach to implement the LUS protocol and score for clinical decision-making in the clinical practice. · LUS is a helpful adjunct for predicting surfactant need in preterm neonates.. · Machine setting variation and probe selection may affect LUS image and score.. · LUS score should be validated at the local unit before clinical implementation..

Exploring the Feasibility of At-Home Lung Ultra-Portable Ultrasound: Parent-Performed Pediatric Lung Imaging.

To determine if caregivers would be able to successfully perform in home lung ultrasounds on their children without direct supervision after undergoing a basic tutorial that would allow for expert interpretation. A prospective exploratory single-center cohort study was conducted on patients (0-18 years) presenting to a pediatric emergency department with a respiratory complaint or COVID-related illness. Caregivers underwent a brief hands-on session and were instructed to scan the lungs daily for 7 days. Images were assessed using a modified POCUS IQ score. Descriptive statistics were used to describe the data and bivariate analysis was used to compare groups. Eighteen patients were enrolled; the average age of the parent scanner was 31.9 years and 78% were female. Of all participants, 77.8% scanned on day one. Parents were able to successfully perform some part of the daily scan session for an average of 3.8 out of 7 days. The average POCUS IQ score overall was 6.7 (out of 12). Our study demonstrates the feasibility and acceptability of caregiver ability to obtain adequate lung ultrasound images, at home under no guidance, using the Butterfly iQ probe. Further studies are needed to investigate the accessibility of ultra-portable ultrasound and the ability to integrate with the at-home hospital model, specifically in the pediatric population.

Quantitative assessment of pneumothorax by using Shannon entropy of lung ultrasound M-mode image and diaphragmatic excursion based on automated measurement.

Lung ultrasound (LUS) and diaphragm ultrasound (DUS) are the appropriate modalities for conservative observation to those patients who are with stable pneumothorax, as well as for the timely detection of life-threatening pneumothorax at any location, due to they are portable, real-time, relatively cost effective, and most important, without radiation exposure. The absence of lung sliding on LUS M-mode images and the abnormality of diaphragmatic excursion (DE) on DUS M-mode images are the most common and novel diagnostic criteria for pneumothorax, respectively. However, visual inspection of M-mode images remains subjective and quantitative analysis of LUS and DUS M-mode images are required. Shannon entropy of LUS M-mode image (ShanEnLM) and DE based on the automated measurement (DEAM) are adapted to the objective pneumothorax diagnoses and the severity quantifications in this study. Mild, moderate, and severe pneumothoraces were induced in 24 male New Zealand rabbits through insufflation of room air (5, 10 and 15, and 25 and 40 mL/kg, respectively) into their pleural cavities. In vivo intercostal LUS and subcostal DUS M-mode images were acquired using a point-of-care system for estimating ShanEnLM and DEAM. ShanEnLM and DEAM as functions of air insufflation volumes exhibited U-shaped curves and were exponentially decreasing, respectively. Either ShanEnLM or DEAM had areas under the receiver operating characteristic curves [95% confidence interval (CI)] of 1.0000 (95% CI: 1.0000-1.0000), 0.9833 (95% CI: 0.9214-1.0000), and 0.9407 (95% CI: 0.8511-1.0000) for differentiating between normal and mild pneumothorax, mild and moderate pneumothoraces, and moderate and severe pneumothoraces, respectively. Our findings imply that the combination of ShanEnLM and DEAM give the promising potential for pneumothorax quantitative diagnosis.

Lung ultrasound imaging and clinical consequences of intraoperative high inspired oxygen fraction in healthy mechanically ventilated children

Study objectiveHigh inspired oxygen concentrations (FiO2) are claimed to cause resorption atelectasis increasing the risk for perioperative hypoxemia and postoperative pulmonary complications. Pediatric physicians are still reluctant to accomplish low FiO2 strategies in children. We investigated the association between lung ultrasound score (LUS) and arterial oxygenation (PaO2/FiO2 ratio)in anesthetized mechanically ventilated children undergoing non-abdominal surgery using different FiO2 fractions. Study designObservational cohort. Study settingsOperative rooms and post-anesthesia care unit. PatientsThirty-three patients aged (3–12 years) of both sexes. InterventionPatients were anesthetized while receiving high FIO2 protocol (preoxygenation 1.0, induction and recovery 0.8, maintenance 0.6, post-extubation 1.0 and 0.21 for 2h postoperative). Measurements: LUS was performed after intubation and initiation of mechanical ventilation, after the end of surgery, and 2h postoperatively. Arterial blood gas analysis was performed at the same time points and respiratory mechanics were recorded intraoperatively. LUS scores were tested for probability of bivariate correlation with PaO2/FiO2 ratio (primary endpoint), age, weight, operation time, PaO2, and dynamic compliance. Main resultsAfter intubation, the median LUS score was 2 (Range: 0–14) which significantly decreased after surgery to 1 (0–8), (P = 0.010). At 2- hours postoperatively, it was 0 (0–6) which was significantly lower than both after intubation (P = 0.001) and after surgery (P = 0.007). No significant Correlation was found between the LUS score and any investigated parameters. ConclusionsEven though we found no significant correlation between LUS and PaO2, or PaO2/FiO2 ratio, the high values of LUS after intubation necessitate caution when administering high FiO2 levels in pediatrics. Trial registrationClinicalTrials.gov ID: NCT04581226.