Computer-Aided Quantitative Ultrasonography for Detection of Pulmonary Edema in Mechanically Ventilated Cardiac Surgery Patients

MUltiparametric Score for Ventilation Discontinuation in Intensive Care Patients: A Protocol for an Observational Study

BACKGROUND

Mechanical ventilation significantly improves patient survival but is associated with complications, increasing healthcare costs and morbidity. Identifying optimal weaning times is paramount to minimize these risks, yet current methods rely heavily on clinical judgment, lacking specificity.

METHODS

This study introduces a novel multiparametric predictive score, the MUSVIP (MUltiparametric Score for Ventilation discontinuation in Intensive care Patients), aimed at accurately predicting successful extubation. Conducted at Santo Stefano Hospital's ICU, this single-center, observational, prospective cohort study will span over 12 months, enrolling adult patients undergoing invasive mechanical ventilation. The MUSVIP integrates variables measured before and during a spontaneous breathing trial (SBT) to formulate a predictive score.

RESULTS

Preliminary analyses suggest an Area Under the Curve (AUC) of 0.815 for the MUSVIP, indicating high predictive capacity. By systematically applying this score, we anticipate identifying patients likely to succeed in weaning earlier, potentially reducing ICU length of stay and associated healthcare costs.

CONCLUSION

This study's findings could significantly influence clinical practices, offering a robust, easy-to-use tool for optimizing weaning processes in ICUs.

Complex Heart-Lung Ventilator Emergencies in the CICU

This review aims to enhance the comprehension and management of cardiopulmonary interactions in critically ill patients with cardiovascular disease undergoing mechanical ventilation. Highlighting the significance of maintaining a delicate balance, this article emphasizes the crucial role of adjusting ventilation parameters based on both invasive and noninvasive monitoring. It provides recommendations for the induction and liberation from mechanical ventilation. Special attention is given to the identification of auto-PEEP (positive end-expiratory pressure) and other situations that may impact hemodynamics and patients' outcomes.

B-lines by lung ultrasound as a predictor of re-intubation in mechanically ventilated patients with heart failure

Introduction

There have been few studies on predictors of weaning failure from MV in patients with heart failure (HF). We sought to investigate the predictive value of B-lines measured by lung ultrasound (LUS) on the risk of weaning failure from mechanical ventilation (MV) and in-hospital outcomes.

Methods

This was a single-center, prospective observational study that included HF patients who were on invasive MV. LUS was performed immediate before ventilator weaning. A positive LUS exam was defined as the observation of two or more regions that had three or more count of B-lines located bilaterally on the thorax. The primary outcome was early MV weaning failure, defined as re-intubation within 72 h.

Results

A total of 146 consecutive patients (mean age 70 years; 65.8% male) were enrolled. The total count of B-lines was a median of 10 and correlated with NT-pro-BNP level (r2 = 0.132, p < 0.001). Early weaning failure was significantly higher in the positive LUS group (9 out of 64, 14.1%) than the negative LUS group (2 out of 82, 2.4%) (p = 0.011). The rate of total re-intubation during the hospital stay (p = 0.004), duration of intensive care unit stay (p = 0.004), and hospital stay (p = 0.010) were greater in the positive LUS group. The negative predictive value (NPV) of positive LUS was 97.6% for the primary outcome.

Conclusion

B-lines measured by LUS can predict the risk of weaning failure. Considering the high NPV of positive LUS, it may help guide the decision of weaning in patients on invasive MV due to acute decompensated HF.

The role of lung ultrasound for detecting atelectasis, consolidation, and/or pneumonia in the adult cardiac surgery population: A scoping review of the literature

OBJECTIVES

Postoperative pulmonary complications (PPCs) frequently occur after cardiac surgery and may lead to adverse patient outcomes. Traditional diagnostic tools such as auscultation or chest x-ray have inferior diagnostic accuracy compared to the gold standard (chest computed tomography). Lung ultrasound (LUS) is an emerging area of research combating these issues. However, no review has employed a formal search strategy to examine the role of LUS in identifying the specific PPCs of atelectasis, consolidation, and/or pneumonia or investigated the ability of LUS to predict these complications in this cohort. The objective of this study was to collate and present evidence for the use of LUS in the adult cardiac surgery population to specifically identify atelectasis, consolidation, and/or pneumonia.

REVIEW METHOD USED

A scoping review of the literature was completed using predefined search terms across six databases which identified 1432 articles. One additional article was included from reviewing reference lists. Six articles met the inclusion criteria, providing sufficient data for the final analysis.

DATA SOURCES

Six databases were searched: MEDLINE, Embase, CINAHL, Scopus, CENTRAL, and PEDro. This review was not registered.

REVIEW METHODS

The review followed the PRISMA Extension for Scoping Reviews.

RESULTS

Several LUS methodologies were reported across studies. Overall, LUS outperformed all other included bedside diagnostic tools, with superior diagnostic accuracy in identifying atelectasis, consolidation, and/or pneumonia. Incidences of PPCs tended to increase with each subsequent timepoint after surgery and were better identified with LUS than all other assessments. A change in diagnosis occurred at a rate of 67% with the inclusion of LUS and transthoracic echocardiography in one study. Pre-established assessment scores were improved by substituting chest x-rays with LUS scans.

CONCLUSION

The results of this scoping review support the use of LUS as a diagnostic tool after cardiac surgery; however, they also highlighted a lack of consistent methodologies used. Future research is required to determine the optimal methodology for LUS in diagnosing PPCs in this cohort and to determine whether LUS possesses the ability to predict these complications and guide proactive respiratory supports after extubation.

Ultrasound imaging of lung disease and its relationship to histopathology: An experimentally validated simulation approach

Lung ultrasound (LUS) is a widely used technique in clinical lung assessment, yet the relationship between LUS images and the underlying disease remains poorly understood due in part to the complexity of the wave propagation physics in complex tissue/air structures. Establishing a clear link between visual patterns in ultrasound images and underlying lung anatomy could improve the diagnostic accuracy and clinical deployment of LUS. Reverberation that occurs at the lung interface is complex, resulting in images that require interpretation of the artifacts deep in the lungs. These images are not accurate spatial representations of the anatomy due to the almost total reflectivity and high impedance mismatch between aerated lung and chest wall. Here, we develop an approach based on the first principles of wave propagation physics in highly realistic maps of the human chest wall and lung to unveil a relationship between lung disease, tissue structure, and its resulting effects on ultrasound images. It is shown that Fullwave numerical simulations of ultrasound propagation and histology-derived acoustical maps model the multiple scattering physics at the lung interface and reproduce LUS B-mode images that are comparable to clinical images. However, unlike clinical imaging, the underlying tissue structure model is known and controllable. The amount of fluid and connective tissue components in the lung were gradually modified to model disease progression, and the resulting changes in B-mode images and non-imaging reverberation measures were analyzed to explain the relationship between pathological modifications of lung tissue and observed LUS.

Echo intensity and gray-level co-occurrence matrix analysis of soft tissue grafting biomaterials and dental implants: an in vitro ultrasonographic pilot study

OBJECTIVE

To characterize different allogeneic and xenogeneic soft tissue graft substitutes and to assess their echo intensity and grayscale texture-related outcomes by using high-frequency ultrasonography (HFUS).

METHODS

Ten samples from each of the following biomaterials were scanned using HFUS: bilayered collagen matrix (CM), cross-linked collagen matrix (CCM), multilayered cross-linked collagen matrix (MCCM), human-derived acellular dermal matrix (HADM), porcine-derived acellular dermal matrix (PADM), collagen tape dressing (C) and dental implants (IMPs). The obtained images were then imported in a commercially available software for grayscale analysis. First-order grayscale outcomes included mean echo intensity (EI), standard deviation, skewness, and kurtosis, while second-order grayscale outcomes comprised entropy, contrast, correlation, energy and homogeneity derive from the gray-level co-occurrence matrix analysis. Descriptive statistics were performed for visualization of results, and one-way analysis of variance with Bonferroni post-hoc tests were performed to relative assessments of the biomaterials.

RESULTS

The statistical analysis revealed a statistically significant difference among the groups for EI (p < .001), with the group C showing the lowest EI, and the IMP group presenting with the greatest EI values. All groups showed significantly higher EI when compared with C (p < .001). No significant differences were observed for energy, and correlation, while a statistically significant difference among the groups was found in terms of entropy (p < 0.01), contrast (p < .001) and homogeneity (p < .001). IMP exhibited the highest contrast, that was significantly higher than C, HADM, PADM, CCM and CM.

CONCLUSIONS

HFUS grayscale analysis can be applied to characterize the structure of different biomaterials and holds potential for translation to in-vivo assessment following soft tissue grafting-related procedures.

The future of neonatal lung ultrasound: Validation of an artificial intelligence model for interpreting lung scans. A multicentre prospective diagnostic study

BACKGROUND

Artificial intelligence (AI) is a promising field in the neonatal field. We focused on lung ultrasound (LU), a useful tool for the neonatologist. Our aim was to train a neural network to create a model able to interpret LU.

METHODS

Our multicentric, prospective study included newborns with gestational age (GA) ≥ 33 + 0 weeks with early tachypnea/dyspnea/oxygen requirements. For each baby, three LU were performed: within 3 h of life (T0), at 4-6 h of life (T1), and in the absence of respiratory support (T2). Each scan was processed to extract the region of interest used to train a neural network to classify it according to the LU score (LUS). We assessed sensitivity, specificity, positive and negative predictive value of the AI model's scores in predicting the need for respiratory assistance with nasal continuous positive airway pressure and for surfactant, compared to an already studied and established LUS.

RESULTS

We enrolled 62 newborns (GA = 36 ± 2 weeks). In the prediction of the need for CPAP, we found a cutoff of 6 (at T0) and 5 (at T1) for both the neonatal lung ultrasound score (nLUS) and AI score (AUROC 0.88 for T0 AI model, 0.80 for T1 AI model). For the outcome "need for surfactant therapy", results in terms of area under receiver operator characteristic (AUROC) are 0.84 for T0 AI model and 0.89 for T1 AI model. In the prediction of surfactant therapy, we found a cutoff of 9 for both scores at T0, at T1 the nLUS cutoff was 6, while the AI's one was 5. Classification accuracy was good both at the image and class levels.

CONCLUSIONS

This is, to our knowledge, the first attempt to use an AI model to interpret early neonatal LUS and can be extremely useful for neonatologists in the clinical setting.

Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound

Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising 1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines. Based on this dataset, we present a benchmark of established deep learning methods applied to the task of B-line detection. To pave the way for interpretable quantification of B-lines, we propose a novel "single-point" approach to B-line localization using only the point of origin. Our results show that (a) the area under the receiver operating characteristic curve ranges from 0.864 to 0.955 for the benchmarked detection methods, (b) within this range, the best performance is achieved by models that leverage multiple successive frames as input, and (c) the proposed single-point approach for B-line localization reaches an F 1-score of 0.65, performing on par with the inter-observer agreement. The dataset and developed methods can facilitate further biomedical research on automated interpretation of lung ultrasound with the potential to expand the clinical utility.

Diagnosing Lung Abnormalities Related to Heart Failure in Chest Radiogram, Lung Ultrasound and Thoracic Computed Tomography

Heart failure (HF) is a multidisciplinary disease affecting almost 1-2% of the adult population worldwide. Symptoms most frequently reported by patients suffering from HF include dyspnoea, cough or exercise intolerance, which is equally often observed in many pulmonary diseases. The spectrum of lung changes related to HF is wide. The knowledge of different types of these abnormalities is essential to distinguish patients with HF from patients with lung diseases or both disorders and thus avoid unnecessary diagnostics or therapies. In this review, we aimed to summarise recent research concerning the spectrum of lung abnormalities related to HF in three frequently used lung imaging techniques: chest X-ray (CXR), lung ultrasound (LUS) and chest computed tomography (CT). We discussed the most prevalent abnormalities in the above-mentioned investigations in the context of consecutive pathophysiological stages identified in HF: (i) redistribution, (ii) interstitial oedema, and (iii) alveolar oedema. Finally, we compared the utility of these imaging tools in the clinical setting. In conclusion, we consider LUS the most useful and promising imaging technique due to its high sensitivity, repeatability and accessibility. However, the value of CXR and chest CT is their potential for establishing a differential diagnosis.

Implementation and Assessment of Lung Ultrasound Training Curriculum for Physiotherapists With a Focus on Image Acquisition and Calculation of an Aeration Score

Described here is the implementation of a lung ultrasound course for physiotherapists focused on the acquisition and retention of knowledge and skills. Initially, we provided online lectures in a virtual learning environment (VLE), in which we taught the semiquantification of edema through a lung ultrasound score (LUS). Afterward, the physiotherapists participated in face-to-face lectures (which resumed the online lectures), followed by hands-on training and simulation with ultrasound. We assessed knowledge acquisition through a multiple-choice test with 30 questions (totaling 10 points). The test was applied before accessing the VLE (pre-VLE), before the face-to-face course and at its end (pre- and post-course). Physiotherapists collected actual patients' ultrasound scans, which were uploaded to the VLE and assessed by three supervisors, who performed a consensus LUS calculation and gave virtual written feedback. Thirteen physiotherapists collected 59 exams. The test results were 3.60 ± 1.58 (pre-VLE), 5.94 ± 1.45 (pre-course) and 8.50 ± 0.71 (post-course), with p &lt; 0.001 for all. The intraclass correlation coefficient for LUS between physiotherapists and supervisors was 0.814 (p &lt; 0.001), with moderate-to-weak agreement for LUS of the lung apical, median and basal zones, with κ = 0.455.334, and 0.417 (p &lt; 0.001 for all). Trainees were found to have increased short-term acquisition and retention of knowledge and skills, with a good intraclass correlation coefficient between them and the consensus of supervisors for the LUS of actual patients.

Quantitative lung ultrasound detects dynamic changes in lung recruitment in the preterm lamb

BACKGROUND

Lung ultrasound (LUS) may not detect small, dynamic changes in lung volume. Mean greyscale measurement using computer-assisted image analysis (Q-LUS_MGV) may improve the precision of these measurements.

METHODS

Preterm lambs (n = 40) underwent LUS of the dependent or non-dependent lung during static pressure-volume curve mapping. Total and regional lung volumes were determined using the super-syringe technique and electrical impedance tomography. Q-LUS_MGV and gold standard measurements of lung volume were compared in 520 images.

RESULTS

Dependent Q-LUS_MGV moderately correlated with total lung volume (rho = 0.60, 95% CI 0.51-0.67) and fairly with right whole (rho = 0.39, 0.27-0.49), central (rho = 0.38, 0.27-0.48), ventral (rho = 0.41, 0.31-0.51) and dorsal regional lung volumes (rho = 0.32, 0.21-0.43). Non-dependent Q-LUS_MGV moderately correlated with total lung volume (rho = 0.57, 0.48-0.65) and fairly with right whole (rho = 0.43, 0.32-0.52), central (rho = 0.46, 0.35-0.55), ventral (rho = 0.36, 0.25-0.47) and dorsal lung volumes (rho = 0.36, 0.25-0.47). All correlation coefficients were statistically significant. Distinct inflation and deflation limbs, and sonographic pulmonary hysteresis occurred in 95% of lambs. The greatest changes in Q-LUS_MGV occurred at the opening and closing pressures.

CONCLUSION

Q-LUS_MGV detected changes in total and regional lung volume and offers objective quantification of LUS images, and may improve bedside discrimination of real-time changes in lung volume.

IMPACT

Lung ultrasound (LUS) offers continuous, radiation-free imaging that may play a role in assessing lung recruitment but may not detect small changes in lung volume. Mean greyscale image analysis using computer-assisted quantitative LUS (Q-LUS_MGV) moderately correlated with changes in total and regional lung volume. Q-LUS_MGV identified opening and closing pressure and pulmonary hysteresis in 95% of lambs. Computer-assisted image analysis may enhance LUS estimation of lung recruitment at the bedside. Future research should focus on improving precision prior to clinical translation.

Quantitative Assessment of Lung Ultrasound Grayscale Images Based on Shannon Entropy for the Detection of Pulmonary Aeration: An Animal Study

OBJECTIVE

Lung ultrasound (LUS) is a radiation-free, affordable, and bedside monitoring method that can detect changes in pulmonary aeration before hypoxic damage. However, visual scoring methods of LUS only enable subjective diagnosis. Therefore, quantitative analysis of LUS is necessary for obtaining objective information on pulmonary aeration. Because raw data are not always available in conventional ultrasound systems, Shannon entropy (ShanEn) of information theory without the requirement of raw data is valuable. In this study, we explored the feasibility of ShanEn estimated through grayscale histogram (GSH) analysis of LUS images for the quantification of pulmonary aeration.

METHODS

Different degrees of pulmonary aeration caused by edema was induced in 32 male New Zealand rabbits intravenously injected with 0.1 mL/kg saline (the control group) and 0.025, 0.05, and 0.1 mL/kg oleic acid (mild, moderate, and severe groups, respectively). In vivo grayscale LUS images were acquired using a commercial point-of-care ultrasound system for estimation of GSH and corresponding ShanEn. Both lungs of each rabbit were dissected, weighed, and dried to determine the wet weight-to-dry weight ratio (W/D) through gravimetry.

RESULTS

The determination coefficients of linear correlations between ShanEn and W/D increased from 0.0487 to 0.7477 with gain and dynamic range (DR). In contrast to visual scoring methods of pulmonary aeration that use median gain and low DR, ShanEn for quantifying pulmonary aeration requires high gain and DR.

CONCLUSION

The current findings indicate that ShanEn estimated through GSH analysis of LUS images acquired using conventional ultrasonic imaging systems has great potential to provide objective information on pulmonary aeration.

The weaning from mechanical ventilation: a comprehensive ultrasound approach

PURPOSE OF REVIEW

Due to heart, lung and diaphragm interactions during weaning from mechanical ventilation, an ultrasound integrated approach may be useful in the detection of dysfunctions potentially leading to weaning failure. In this review, we will summarize the most recent advances concerning the ultrasound applications relevant to the weaning from mechanical ventilation.

RECENT FINDINGS

The role of ultrasonographic examination of heart, lung and diaphragm has been deeply investigated over the years. Most recent findings concern the ability of lung ultrasound in detecting weaning induced pulmonary edema during spontaneous breathing trial. Furthermore, in patients at high risk of cardiac impairments, global and anterolateral lung ultrasound scores have been correlated with weaning and extubation failure, whereas echocardiographic indexes were not. For diaphragmatic ultrasound evaluation, new indexes have been proposed for the evaluation of diaphragm performance during weaning, but further studies are needed to validate these results.

SUMMARY

The present review summarizes the potential role of ultrasonography in the weaning process. A multimodal integrated approach allows the clinician to comprehend the pathophysiological processes of weaning failure.

Visual Rounds Based on Multiorgan Point-of-Care Ultrasound in the ICU

Point-of-care ultrasonography (POCUS) is performed by a treating clinician at the patient's bedside, provides a acquisition, interpretation, and immediate clinical integration based on ultrasonographic imaging. The use of POCUS is not limited to one specialty, protocol, or organ system. POCUS provides the treating clinician with real-time diagnostic and monitoring information. Visual rounds based on multiorgan POCUS act as an initiative to improve clinical practice in the Intensive Care Unit and are urgently needed as part of routine clinical practice.

Evaluation of Pulmonary Edema Using Ultrasound Imaging in Patients With COVID-19 Pneumonia Based on a Non-local Channel Attention ResNet

Recent research has revealed that COVID-19 pneumonia is often accompanied by pulmonary edema. Pulmonary edema is a manifestation of acute lung injury (ALI), and may progress to hypoxemia and potentially acute respiratory distress syndrome (ARDS), which have higher mortality. Precise classification of the degree of pulmonary edema in patients is of great significance in choosing a treatment plan and improving the chance of survival. Here we propose a deep learning neural network named Non-local Channel Attention ResNet to analyze the lung ultrasound images and automatically score the degree of pulmonary edema of patients with COVID-19 pneumonia. The proposed method was designed by combining the ResNet with the non-local module and the channel attention mechanism. The non-local module was used to extract the information on characteristics of A-lines and B-lines, on the basis of which the degree of pulmonary edema could be defined. The channel attention mechanism was used to assign weights to decisive channels. The data set contains 2220 lung ultrasound images provided by Huoshenshan Hospital, Wuhan, China, of which 2062 effective images with accurate scores assigned by two experienced clinicians were used in the experiment. The experimental results indicated that our method achieved high accuracy in classifying the degree of pulmonary edema in patients with COVID-19 pneumonia by comparison with previous deep learning methods, indicating its potential to monitor patients with COVID-19 pneumonia.

Ten conditions where lung ultrasonography may fail: limits, pitfalls and lessons learned from a computer-aided algorithmic approach

Lung ultrasonography provides relevant information on morphological and functional changes occurring in the lungs. However, it correlates weakly with pulmonary congestion and extra vascular lung water. Moreover, there is lack of consensus on scoring systems and acquisition protocols. The automation of this technique may provide promising easy-to-use clinical tools to reduce inter- and intra-observer variability and to standardize scores, allowing faster data collection without increased costs and patients risks.

Quantifying lung aeration in neonatal lambs at birth using lung ultrasound

BACKGROUND

Lung ultrasound (LUS) is a safe and non-invasive tool that can potentially assess regional lung aeration in newborn infants and reduce the need for X-ray imaging. LUS produces images with characteristic artifacts caused by the presence of air in the lung, but it is unknown if LUS can accurately detect changes in lung air volumes after birth. This study compared LUS images with lung volume measurements from high-resolution computed tomography (CT) scans to determine if LUS can accurately provide relative measures of lung aeration.

METHODS

Deceased near-term newborn lambs (139 days gestation, term ∼148 days) were intubated and the chest imaged using LUS (bilaterally) and phase contrast x-ray CT scans at increasing static airway pressures (0-50 cmH₂O). CT scans were analyzed to calculate regional air volumes and correlated with measures from LUS images. These measures included (i) LUS grade; (ii) brightness (mean and coefficient of variation); and (iii) area under the Fourier power spectra within defined frequency ranges.

RESULTS

All LUS image analysis techniques correlated strongly with air volumes measured by CT (p < 0.01). When imaging statistics were combined in a multivariate linear regression model, LUS predicted the proportion of air in the underlying lung with moderate accuracy (95% prediction interval ± 22.15%, r ² = 0.71).

CONCLUSION

LUS can provide relative measures of lung aeration after birth in neonatal lambs. Future studies are needed to determine if LUS can also provide a simple means to assess air volumes and individualize aeration strategies for critically ill newborns in real time.

Utility of Lung Ultrasound in the Estimation of Extravascular Lung Water in a Pediatric Population-A Prospective Observational Study

OBJECTIVE

Lung ultrasound (LUS) is a promising bedside modality for the estimation of extravascular lung water index (EVLWI), but has not been validated against objective measures in children. This study aimed to investigate the correlation of LUS B-line scoring with EVLWI, thresholds indicating elevated EVLWI, and its outcome following pediatric cardiac surgery.

DESIGN

Prospective observational study.

SETTING

Cardiothoracic surgical intensive care unit in a tertiary care teaching hospital.

PARTICIPANTS

Children younger than 12 years undergoing elective complete surgical correction of cyanotic or acyanotic congenital heart disease (Aristotle score ≤9), excluding neonates, those weighing <3.5 kg, and those with thoracic deformities, pulmonary pathology, and hemodynamic instability.

INTERVENTIONS

Extravascular lung water index measurement by transpulmonary thermodilution, along with concurrent LUS B-line and Chest-X ray (CXR) scoring.

MEASUREMENTS AND MAIN RESULTS

LUS B-line score had a moderate correlation with EVLWI (Pearson's correlation coefficient 0.57; 95% CI 0.44-0.69). LUS B-line scores showed acceptable discrimination only for higher thresholds of EVLWI (sensitivity 82% and 79%, respectively, for EVLWI >20 mL/kg v sensitivity and specificity 57% and 80% for EVLWI >10 mL/kg). Age, body surface area, vasoactive-inotropic score (VIS), chest X-ray score, and EVLWI but not LUS B-line score were significant predictors for duration of mechanical ventilation in this cohort.

CONCLUSIONS

LUS B-line scoring has limited utility in semiquantitative estimation of EVLWI at lower thresholds of EVLWI in pediatric cardiac surgical patients. It may have better discrimination and acceptable sensitivity and specificity at higher thresholds of EVLWI. Contrasting with multiple reports of clinical utility, these results call for wider evaluation of LUS and its clinical modifiers like age, pathology, and pretest probability in estimation of EVLWI.

Lung Ultrasound Assessment of Focal and Non-focal Lung Morphology in Patients With Acute Respiratory Distress Syndrome

Background: The identification of phenotypes based on lung morphology can be helpful to better target mechanical ventilation of individual patients with acute respiratory distress syndrome (ARDS). We aimed to assess the accuracy of lung ultrasound (LUS) methods for classification of lung morphology in critically ill ARDS patients under mechanical ventilation.

Methods: This was a post hoc analysis on two prospective studies that performed LUS and chest computed tomography (CT) scanning at the same time. Expert panels from the two participating centers separately developed two LUS methods for classifying lung morphology based on LUS aeration scores from a 12-region exam (Amsterdam and Lombardy method). Moreover, a previously developed LUS method based on anterior LUS scores was tested (Piedmont method). Sensitivity and specificity of all three LUS methods was assessed in the cohort of the other center(s) by using CT as the gold standard for classification of lung morphology.

Results: The Amsterdam and Lombardy cohorts consisted of 32 and 19 ARDS patients, respectively. From these patients, 23 (45%) had focal lung morphology while others had non-focal lung morphology. The Amsterdam method could classify focal lung morphology with a sensitivity of 77% and a specificity of 100%, while the Lombardy method had a sensitivity and specificity of 100 and 61%. The Piedmont method had a sensitivity and specificity of 91 and 75% when tested on both cohorts. With both the Amsterdam and Lombardy method, most patients could be classified based on the anterior regions alone.

Conclusion: LUS-based methods can accurately classify lung morphology in invasively ventilated ARDS patients compared to gold standard chest CT. The anterior LUS regions showed to be the most discriminant between focal and non-focal lung morphology, although accuracy increased moderately when lateral and posterior LUS regions were integrated in the method.

Ultrasound versus Computed Tomography Assessment of Focal Lung Aeration in Invasively Ventilated ICU Patients

It is unknown whether and to what extent the penetration depth of lung ultrasound (LUS) influences the accuracy of LUS findings. The current study evaluated and compared the LUS aeration score and two frequently used B-line scores with focal lung aeration assessed by chest computed tomography (CT) at different levels of depth in invasively ventilated intensive care unit (ICU) patients. In this prospective observational study, patients with a clinical indication for chest CT underwent a 12-region LUS examination shortly before CT scanning. LUS images were compared with corresponding regions on the chest CT scan at different subpleural depths. For each LUS image, the LUS aeration score was calculated. LUS images with B-lines were scored as the number of separately spaced B-lines (B-line count score) and the percentage of the screen covered by B-lines divided by 10 (B-line percentage score). The fixed-effect correlation coefficient (β) was presented per 100 Hounsfield units. A total of 40 patients were included, and 372 regions were analyzed. The best association between the LUS aeration score and CT was found at a subpleural depth of 5 cm for all LUS patterns (β = 0.30, p < 0.001), 1 cm for A- and B1-patterns (β = 0.10, p < 0.001), 6 cm for B1- and B2-patterns (β = 0.11, p < 0.001) and 4 cm for B2- and C-patterns (β = 0.07, p = 0.001). The B-line percentage score was associated with CT (β = 0.46, p = 0.001), while the B-line count score was not (β = 0.07, p = 0.305). In conclusion, the subpleural penetration depth of ultrasound increased with decreased aeration reflected by the LUS pattern. The LUS aeration score and the B-line percentage score accurately reflect lung aeration in ICU patients, but should be interpreted while accounting for the subpleural penetration depth of ultrasound.

Quantitative Lung Ultrasound: Technical Aspects and Clinical Applications

Lung ultrasound is increasingly used in emergency departments, medical wards, and critical care units-adult, pediatric, and neonatal. In vitro and in vivo studies show that the number and type of artifacts visualized change with lung density. This has led to the idea of a quantitative lung ultrasound approach, opening up new prospects for use not only as a diagnostic but also as a monitoring tool. Consequently, the multiple scoring systems proposed in the last few years have different technical approaches and specific clinical indications, adaptable for more or less time-dependent patients. However, multiple scoring systems may generate confusion among physicians aiming at introducing lung ultrasound in their clinical practice. This review describes the various lung ultrasound scoring systems and aims to clarify their use in different settings, focusing on technical aspects, validation with reference techniques, and clinical applications.

Quantitative Analysis and Automated Lung Ultrasound Scoring for Evaluating COVID-19 Pneumonia With Neural Networks

As being radiation-free, portable, and capable of repetitive use, ultrasonography is playing an important role in diagnosing and evaluating the COVID-19 Pneumonia (PN) in this epidemic. By virtue of lung ultrasound scores (LUSS), lung ultrasound (LUS) was used to estimate the excessive lung fluid that is an important clinical manifestation of COVID-19 PN, with high sensitivity and specificity. However, as a qualitative method, LUSS suffered from large interobserver variations and requirement for experienced clinicians. Considering this limitation, we developed a quantitative and automatic lung ultrasound scoring system for evaluating the COVID-19 PN. A total of 1527 ultrasound images prospectively collected from 31 COVID-19 PN patients with different clinical conditions were evaluated and scored with LUSS by experienced clinicians. All images were processed via a series of computer-aided analysis, including curve-to-linear conversion, pleural line detection, region-of-interest (ROI) selection, and feature extraction. A collection of 28 features extracted from the ROI was specifically defined for mimicking the LUSS. Multilayer fully connected neural networks, support vector machines, and decision trees were developed for scoring LUS images using the fivefold cross validation. The model with 128×256 two fully connected layers gave the best accuracy of 87%. It is concluded that the proposed method could assess the ultrasound images by assigning LUSS automatically with high accuracy, potentially applicable to the clinics.

B-line quantification: comparing learners novice to lung ultrasound assisted by machine artificial intelligence technology to expert review

BACKGROUND

The goal of this study was to assess the ability of machine artificial intelligence (AI) to quantitatively assess lung ultrasound (LUS) B-line presence using images obtained by learners novice to LUS in patients with acute heart failure (AHF), compared to expert interpretation.

METHODS

This was a prospective, multicenter observational study conducted at two urban academic institutions. Learners novice to LUS completed a 30-min training session on lung image acquisition which included lecture and hands-on patient scanning. Learners independently acquired images on patients with suspected AHF. Automatic B-line quantification was obtained offline after completion of the study. Machine AI counted the maximum number of B-lines visualized during a clip. The criterion standard for B-line counts was semi-quantitative analysis by a blinded point-of-care LUS expert reviewer. Image quality was blindly determined by an expert reviewer. A second expert reviewer blindly determined B-line counts and image quality. Intraclass correlation was used to determine agreement between machine AI and expert, and expert to expert.

RESULTS

Fifty-one novice learners completed 87 scans on 29 patients. We analyzed data from 611 lung zones. The overall intraclass correlation for agreement between novice learner images post-processed with AI technology and expert review was 0.56 (confidence interval [CI] 0.51-0.62), and 0.82 (CI 0.73-0.91) between experts. Median image quality was 4 (on a 5-point scale), and correlation between experts for quality assessment was 0.65 (CI 0.48-0.82).

CONCLUSION

After a short training session, novice learners were able to obtain high-quality images. When the AI deep learning algorithm was applied to those images, it quantified B-lines with moderate-to-fair correlation as compared to semi-quantitative analysis by expert review. This data shows promise, but further development is needed before widespread clinical use.

Lung, Heart, Vascular, and Diaphragm Ultrasound Examination of COVID-19 Patients: A Comprehensive Approach

Lung ultrasound (LU) has a multitude of features and capacities that make it a useful medical tool to assist physicians contending with the pandemic spread of novel coronavirus disease-2019 (COVID-19) caused by coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Thus, an LU approach to patients with suspected COVID-19 is being implemented worldwide. In noncritical COVID-19 patients, 2 new LU signs have been described and proposed, the "waterfall" and the "light beam" signs. Both signs have been hypothesized to increase the diagnostic accuracy of LU for COVID-19 interstitial pneumonia. In critically ill patients, a distinct pattern of LU changes seems to follow the disease's progression, and this information can be used to guide decisions about when a patient needs to be ventilated, as occurs in other disease states similar to COVID-19. Furthermore, a new algorithm has been published, which enables the automatic detection of B-lines as well as quantification of the percentage of the pleural line associated with lung disease. In COVID-19 patients, a direct involvement of cardiac function has been demonstrated, and ventilator-induced diaphragm dysfunction might be present due to the prolonged mechanical ventilation often involved, as reported for similar diseases. For this reason, cardiac and diaphragm ultrasound evaluation are highly important. Last but not least, due to the thrombotic tendency of COVID-19 patients, particular attention also should be paid to vascular ultrasound. This review is primarily devoted to the study of LU in COVID-19 patients. The authors explain the significance of its "light and shadows," bearing in mind the context in which LU is being used-the emergency department and the intensive care setting. The use of cardiac, vascular, and diaphragm ultrasound is also discussed, as a comprehensive approach to patient care.

Lung Ultrasound Surface Wave Elastography for Assessing Patients With Pulmonary Edema

B-Mode ultrasound insonation of lungs that are dense with extravascular lung water (EVLW) produces characteristic reverberation artifacts termed B-lines. The number of B-lines present demonstrates reasonable correlation to the amount of EVLW. However, analysis of B-line artifacts generated by this modality is semi-quantitative relying on visual interpretation, and as a result, can be subject to inter-observer variability. The purpose of this study was to translate the use of a novel, quantitative lung ultrasound surface wave elastography technique (LUSWE) into the bedside assessment of pulmonary edema in patients admitted with acute congestive heart failure. B-mode lung ultrasound and LUSWE assessment of the lungs were performed using anterior and lateral intercostal spaces in the supine patient. 14 patients were evaluated at admission with reassessment performed 1-2 days after initiation of diuretic therapy. Each exam recorded the total lung B-lines, lung surface wave speeds (at 100, 150, and 200 Hz) and net fluid balance. The patient cohort experienced effective diuresis (average net fluid balance of negative 2.1 liters) with corresponding decrease in pulmonary edema visualized by B-mode ultrasound (average decrease of 13 B-Lines). In addition, LUSWE demonstrated a statistically significant reduction in the magnitude of wave speed from admission to follow-up. The reduction in lung surface wave speed suggests a decrease in lung stiffness (decreased elasticity) mediated by successful reduction of pulmonary edema. In summary, LUSWE is a noninvasive technique for quantifying elastic properties of superficial lung tissue that may prove useful as a diagnostic test, performed at the bedside, for the quantitative assessment of pulmonary edema.

Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study

OBJECTIVES

Lung ultrasound (LUS) is a portable, low-cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning (DL) techniques to match or exceed human-level, diagnostic specificity among similar appearing, pathological LUS images.

DESIGN

A convolutional neural network (CNN) was trained on LUS images with B lines of different aetiologies. CNN diagnostic performance, as validated using a 10% data holdback set, was compared with surveyed LUS-competent physicians.

SETTING

Two tertiary Canadian hospitals.

PARTICIPANTS

612 LUS videos (121 381 frames) of B lines from 243 distinct patients with either (1) COVID-19 (COVID), non-COVID acute respiratory distress syndrome (NCOVID) or (3) hydrostatic pulmonary edema (HPE).

RESULTS

The trained CNN performance on the independent dataset showed an ability to discriminate between COVID (area under the receiver operating characteristic curve (AUC) 1.0), NCOVID (AUC 0.934) and HPE (AUC 1.0) pathologies. This was significantly better than physician ability (AUCs of 0.697, 0.704, 0.967 for the COVID, NCOVID and HPE classes, respectively), p<0.01.

CONCLUSIONS

A DL model can distinguish similar appearing LUS pathology, including COVID-19, that cannot be distinguished by humans. The performance gap between humans and the model suggests that subvisible biomarkers within ultrasound images could exist and multicentre research is merited.

The role of ultrasonographic lung aeration score in the prediction of postoperative pulmonary complications: an observational study

Background

Postoperative pulmonary complications (PPCs) are important contributors to mortality and morbidity after surgery. The available predicting models are useful in preoperative risk assessment, but there is a need for validated tools for the early postoperative period as well. Lung ultrasound is becoming popular in intensive and perioperative care and there is a growing interest to evaluate its role in the detection of postoperative pulmonary pathologies.

Objectives

We aimed to identify characteristics with the potential of recognizing patients at risk by comparing the lung ultrasound scores (LUS) of patients with/without PPC in a 24-h postoperative timeframe.

Methods

Observational study at a university clinic. We recruited ASA 2–3 patients undergoing elective major abdominal surgery under general anaesthesia. LUS was assessed preoperatively, and also 1 and 24 h after surgery. Baseline and operative characteristics were also collected. A one-week follow up identified PPC+ and PPC- patients. Significantly differing LUS values underwent ROC analysis. A multi-variate logistic regression analysis with forward stepwise model building was performed to find independent predictors of PPCs.

Results

Out of the 77 recruited patients, 67 were included in the study. We evaluated 18 patients in the PPC+ and 49 in the PPC- group. Mean ages were 68.4 ± 10.2 and 66.4 ± 9.6 years, respectively (p = 0.4829). Patients conforming to ASA 3 class were significantly more represented in the PPC+ group (66.7 and 26.5%; p = 0.0026). LUS at baseline and in the postoperative hour were similar in both populations. The median LUS at 0 h was 1.5 (IQR 1–2) and 1 (IQR 0–2; p = 0.4625) in the PPC+ and PPC- groups, respectively. In the first postoperative hour, both groups had a marked increase, resulting in scores of 6.5 (IQR 3–9) and 5 (IQR 3–7; p = 0.1925). However, in the 24th hour, median LUS were significantly higher in the PPC+ group (6; IQR 6–10 vs 3; IQR 2–4; p < 0.0001) and it was an independent risk factor (OR = 2.6448 CI95% 1.5555–4.4971; p = 0.0003). ROC analysis identified the optimal cut-off at 5 points with high sensitivity (0.9444) and good specificity (0.7755).

Conclusion

Postoperative LUS at 24 h can identify patients at risk of or in an early phase of PPCs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01236-6.

Second-order grey-scale texture analysis of pleural ultrasound images to differentiate acute respiratory distress syndrome and cardiogenic pulmonary edema

Discriminating acute respiratory distress syndrome (ARDS) from acute cardiogenic pulmonary edema (CPE) may be challenging in critically ill patients. Aim of this study was to investigate if gray-level co-occurrence matrix (GLCM) analysis of lung ultrasound (LUS) images can differentiate ARDS from CPE. The study population consisted of critically ill patients admitted to intensive care unit (ICU) with acute respiratory failure and submitted to LUS and extravascular lung water monitoring, and of a healthy control group (HCG). A digital analysis of pleural line and subpleural space, based on the GLCM with second order statistical texture analysis, was tested. We prospectively evaluated 47 subjects: 16 with a clinical diagnosis of CPE, 8 of ARDS, and 23 healthy subjects. By comparing ARDS and CPE patients' subgroups with HCG, the one-way ANOVA models found a statistical significance in 9 out of 11 GLCM textural features. Post-hoc pairwise comparisons found statistical significance within each matrix feature for ARDS vs. CPE and CPE vs. HCG (P ≤ 0.001 for all). For ARDS vs. HCG a statistical significance occurred only in two matrix features (correlation: P = 0.005; homogeneity: P = 0.048). The quantitative method proposed has shown high diagnostic accuracy in differentiating normal lung from ARDS or CPE, and good diagnostic accuracy in differentiating CPE and ARDS. Gray-level co-occurrence matrix analysis of LUS images has the potential to aid pulmonary edemas differential diagnosis.

Quantitative Lung Ultrasound Spectroscopy Applied to the Diagnosis of Pulmonary Fibrosis: The First Clinical Study

The application of ultrasound imaging to the diagnosis of lung diseases is nowadays receiving growing interest. However, lung ultrasound (LUS) is mainly limited to the analysis of imaging artifacts, such as B-lines, which correlate with a wide variety of diseases. Therefore, the results of LUS investigations remain qualitative and subjective, and specificity is obviously suboptimal. Focusing on the development of a quantitative method dedicated to the lung, in this work, we present the first clinical results obtained with quantitative LUS spectroscopy when applied to the differentiation of pulmonary fibrosis. A previously developed specific multifrequency ultrasound imaging technique was utilized to acquire ultrasound images from 26 selected patients. The multifrequency imaging technique was implemented on the ULtrasound Advanced Open Platform (ULA-OP) platform and an LA533 (Esaote, Florence, Italy) linear-array probe was utilized. RF data obtained at different imaging frequencies (3, 4, 5, and 6 MHz) were acquired and processed in order to characterize B-lines based on their frequency content. In particular, B-line native frequencies (the frequency at which a B-line exhibits the highest intensity) and bandwidth (the range of frequencies over which a B-line shows intensities within -6 dB from its highest intensity), as well as B-line intensity, were analyzed. The results show how the analysis of these features allows (in this group of patients) the differentiation of fibrosis with a sensitivity and specificity equal to 92% and 92%, respectively. These promising results strongly motivate toward the extension of the clinical study, aiming at analyzing a larger cohort of patients and including a broader range of pathologies.

Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultrasound Data

Recent works highlighted the significant potential of lung ultrasound (LUS) imaging in the management of subjects affected by COVID-19. In general, the development of objective, fast, and accurate automatic methods for LUS data evaluation is still at an early stage. This is particularly true for COVID-19 diagnostic. In this article, we propose an automatic and unsupervised method for the detection and localization of the pleural line in LUS data based on the hidden Markov model and Viterbi Algorithm. The pleural line localization step is followed by a supervised classification procedure based on the support vector machine (SVM). The classifier evaluates the healthiness level of a patient and, if present, the severity of the pathology, i.e., the score value for each image of a given LUS acquisition. The experiments performed on a variety of LUS data acquired in Italian hospitals with both linear and convex probes highlight the effectiveness of the proposed method. The average overall accuracy in detecting the pleura is 84% and 94% for convex and linear probes, respectively. The accuracy of the SVM classification in correctly evaluating the severity of COVID-19 related pleural line alterations is about 88% and 94% for convex and linear probes, respectively. The results as well as the visualization of the detected pleural line and the predicted score chart, provide a significant support to medical staff for further evaluating the patient condition.

Real-time multi-frequency ultrasound imaging for quantitative lung ultrasound - first clinical results

Lung ultrasound imaging is a fast-evolving field of application for ultrasound technologies. However, most diagnoses are currently performed with imaging protocols that assume a quasi-homogeneous speed of sound in the volume of interest. When applied to the lung, due to the presence of air, this assumption is unrealistic. Consequently, diagnoses are often based on imaging artifacts and thus qualitative and subjective. In this paper, we present an image formation protocol that is capable of capturing the frequency dependence of well-known artifacts (B-lines) and visualizing it in real time, ultimately providing a quantitative assessment of the signals received from the lung. Previous in vitro studies have shown the potential of B-lines native-frequency for the characterization of bubbly medium, but this paper presents the first results on clinical data. The image formation process has been designed to work on lung tissue, and ultrasound images generated with four orthogonal bands centered at 3, 4, 5 and 6 MHz can be acquired and displayed in real time. Results show that B-lines can be characterized on the basis of their native frequency in vivo and open the way toward real-time quantitative lung ultrasound imaging.

On the influence of imaging parameters on lung ultrasound B-line artifacts, in vitro study

The clinical relevance of lung ultrasonography (LUS) has been rapidly growing since the 1990s. However, LUS is mainly based on the evaluation of visual artifacts (also called B-lines), leading to subjective and qualitative diagnoses. The formation of B-lines remains unknown and, hence, researchers need to study their origin to allow clinicians to quantitatively evaluate the state of lungs. This paper investigates an ambiguity about the formation of B-lines, leading to the formulation of two main hypotheses. The first hypothesis states that the visualization of these artifacts is linked only to the dimension of the emitted beam, whereas the second associates their appearance to specific resonance phenomena. To verify these hypotheses, the frequency spectrum of B-lines was studied by using dedicated lung-phantoms. A research programmable platform connected to an LA533 linear array probe was exploited both to implement a multifrequency approach and to acquire raw radio frequency data. The strength of each artifact was measured as a function of frequency, focal point, and transmitting aperture by means of the artifact total intensity. The results show that the main parameter that influences the visualization of B-lines is the frequency rather than the focal point or the number of transmitting elements.

Assessing Extravascular Lung Water in Critically Ill Patients Using Lung Ultrasound: A Systematic Review on Methodological Aspects in Diagnostic Accuracy Studies

Lung ultrasound (LUS) is a non-invasive bedside method used to quantify extravascular lung water (EVLW). To evaluate the methodology and diagnostic accuracy of LUS in studies assessing EVLW in intensive care unit patients, PubMed and Embase were searched for studies comparing LUS with imaging modalities. In 14 relevant studies a wide variety of equipment used and training of examiners were noted. Four scoring systems were reported: (i) a binary score (the presence of three or more B-lines); (ii) a categorical score; (iii) a numerical score; (iv) a quantitative LUS score using software. The diagnostic accuracy of LUS varied: sensitivity ranged from 50%-98%, specificity from 76%-100% and r² from 0.20-0.91. Methodology and diagnostic accuracy varies substantially in published reports. Further research is needed to correlate methodological factors with diagnostic accuracy. Hospitals should standardize LUS methodology. Consensus is needed to harmonize LUS methodology for lung water assessment.

LUCAS (lung ultrasonography in cardiac surgery) score to monitor pulmonary edema after congenital cardiac surgery in children

Aim: Cardiopulmonary bypass (CPB) generates a systemic capillary leak syndrome with pulmonary edema. Lung ultrasound (LUS) could be useful to monitor it. Primary objective was to compare sensitivity, specificity, positive and negative predictive values of chest X-ray and LUS to detect pulmonary edema using a new score (LUCAS). Secondary objectives were to evaluate correlation between LUCAS score and respiratory and inotropic support.Methods: Prospective intervention study including patients <2 months admitted to the Pediatric Intensive Care Unit after CPB. LUS was performed with a lineal probe, screening 3 points in each lung (parasternal, anterolateral and posterior area), pre and post-CPB. Pulmonary edema was evaluated clinically, through LUCAS score and with X-ray.Results: 17 patients were included. LUS achieved higher sensitivity than X-ray to detect pulmonary edema (91.7 versus 44.0%) and greater predictive negative value (88.2 versus 53.3%). There was correlation between higher LUCAS score prior to surgery and longer mechanical ventilation. High values of LUCAS score after surgery correlated with longer CPB time, inotropic support, and FiO₂ need.Conclusion: LUS detected pulmonary edema better than chest X-ray, with greater sensitivity and negative predictive value. LUCAS score was useful to predict more inotropic support and longer mechanical ventilation.Key notesCardiopulmonary bypass during cardiac surgery, generates a systemic capillary leak syndrome with pulmonary edema.In this prospective study performed in the Pediatric Intensive Care Unit, lung ultrasound detected pulmonary edema better than X-ray, with greater sensitivity and negative predictive value.LUCAS score was useful to predict more inotropic support and longer mechanical ventilation.

Imaging the Injured Lung: Mechanisms of Action and Clinical Use

Acute respiratory distress syndrome (ARDS) consists of acute hypoxemic respiratory failure characterized by massive and heterogeneously distributed loss of lung aeration caused by diffuse inflammation and edema present in interstitial and alveolar spaces. It is defined by consensus criteria, which include diffuse infiltrates on chest imaging-either plain radiography or computed tomography. This review will summarize how imaging sciences can inform modern respiratory management of ARDS and continue to increase the understanding of the acutely injured lung. This review also describes newer imaging methodologies that are likely to inform future clinical decision-making and potentially improve outcome. For each imaging modality, this review systematically describes the underlying principles, technology involved, measurements obtained, insights gained by the technique, emerging approaches, limitations, and future developments. Finally, integrated approaches are considered whereby multimodal imaging may impact management of ARDS.

Quantitative lung ultrasonography: a putative new algorithm for automatic detection and quantification of B-lines

BACKGROUND

This pilot study was designed to develop a fully automatic and quantitative scoring system of B-lines (QLUSS: quantitative lung ultrasound score) involving the pleural line and to compare it with previously described semi-quantitative scores in the measurement of extravascular lung water as determined by standard thermo-dilution.

METHODS

This was a prospective observational study of 12 patients admitted in the intensive care unit with acute respiratory distress and each provided with 12 lung ultrasound (LUS) frames. Data collected from each patient consisted in five different scores, four semi-quantitative (nLUSS, cLUSS, qLUSS, %LUSS) and quantitative scores (QLUSS). The association between LUS scores and extravascular lung water (EVLW) was determined by simple linear regression (SLR) and robust linear regression (RLR) methods. A correlation analysis between the LUS scores was performed by using the Spearman rank test. Inter-observer variability was tested by computing intraclass correlation coefficient (ICC) in two-way models for agreement, basing on scores obtained by different raters blinded to patients' conditions and clinical history.

RESULTS

In the SLR, QLUSS showed a stronger association with EVLW (R² = 0.57) than cLUSS (R² = 0.45) and nLUSS (R² = 0.000), while a lower association than qLUSS (R² = 0.85) and %LUSS (R² = 0.72) occurred. By applying RLR, QLUSS showed an association for EVLW (R² = 0.86) comparable to qLUSS (R² = 0.85) and stronger than %LUSS (R² = 0.72). QLUSS was significantly correlated with qLUSS (r = 0.772; p = 0.003) and %LUSS (r = 0.757; p = 0.005), but not with cLUSS (r = 0.561; p = 0.058) and nLUSS (r = 0.105; p = 0.744). Moreover, QLUSS showed the highest ICC (0.998; 95%CI from 0.996 to 0.999) among the LUS scores.

CONCLUSIONS

This study demonstrates that computer-aided scoring of the pleural line percentage affected by B-lines has the potential to assess EVLW. QLUSS may have a significant impact, once validated with a larger dataset composed by multiple real-time frames. This approach has the potentials to be advantageous in terms of faster data analysis and applicability to large sets of data without increased costs. On the contrary, it is not useful in pleural effusion or consolidations.

Localizing B-Lines in Lung Ultrasonography by Weakly Supervised Deep Learning, In-Vivo Results

Lung ultrasound (LUS) is nowadays gaining growing attention from both the clinical and technical world. Of particular interest are several imaging-artifacts, e.g., A- and B- line artifacts. While A-lines are a visual pattern which essentially represent a healthy lung surface, B-line artifacts correlate with a wide range of pathological conditions affecting the lung parenchyma. In fact, the appearance of B-lines correlates to an increase in extravascular lung water, interstitial lung diseases, cardiogenic and non-cardiogenic lung edema, interstitial pneumonia and lung contusion. Detection and localization of B-lines in a LUS video are therefore tasks of great clinical interest, with accurate, objective and timely evaluation being critical. This is particularly true in environments such as the emergency units, where timely decision may be crucial. In this work, we present and describe a method aimed at supporting clinicians by automatically detecting and localizing B-lines in an ultrasound scan. To this end, we employ modern deep learning strategies and train a fully convolutional neural network to perform this task on B-mode images of dedicated ultrasound phantoms in-vitro, and on patients in-vivo. An accuracy, sensitivity, specificity, negative and positive predictive value equal to 0.917, 0.915, 0.918, 0.950 and 0.864 were achieved in-vitro, respectively. Using a clinical system in-vivo, these statistics were 0.892, 0.871, 0.930, 0.798 and 0.958, respectively. We moreover calculate neural attention maps that visualize which components in the image triggered the network, thereby offering simultaneous weakly-supervised localization. These promising results confirm the capability of the proposed method to identify and localize the presence of B-lines in clinical lung ultrasonography.

Effects of crystalloid, hyper-oncotic albumin, and iso-oncotic albumin on lung and kidney damage in experimental acute lung injury

BACKGROUND

Conflicting data have reported beneficial effects of crystalloids, hyper-oncotic albumin (20%ALB), and iso-oncotic albumin (5%ALB) in critically ill patients. Although hyper-oncotic albumin may minimize lung injury, recent studies have shown that human albumin may lead to kidney damage proportional to albumin concentration. In this context, we compared the effects of Ringer's lactate (RL), 20%ALB, and 5%ALB, all titrated according to similar hemodynamic goals, on pulmonary function, lung and kidney histology, and molecular biology in experimental acute lung injury (ALI).

METHODS

Male Wistar rats received Escherichia coli lipopolysaccharide intratracheally (n = 24) to induce ALI. After 24 h, animals were anesthetized and randomly assigned to receive RL, 20%ALB, or 5%ALB (n = 6/group) to maintain hemodynamic stability (distensibility index of inferior vena cava < 25%, mean arterial pressure > 65 mmHg). Rats were then mechanically ventilated for 6 h. Six animals, which received neither ventilation nor fluids (NV), were used for molecular biology analyses.

RESULTS

The total fluid volume infused was higher in RL compared to 5%ALB and 20%ALB (median [interquartile range], 10.8[8.2-33.2] vs. 4.8[3.6-7.7] and 4.3[3.9-6.6] mL, respectively; p = 0.02 and p = 0.003). B-line counts on lung ultrasound (p < 0.0001 and p = 0.0002) and serum lactate levels (p = 0.01 and p = 0.01) were higher in RL than 5%ALB and 20%ALB. Diffuse alveolar damage score was lower in 5%ALB (10.5[8.5-12]) and 20%ALB (10.5[8.5-14]) than RL (16.5[12.5-20.5]) (p < 0.05 and p = 0.03, respectively), while acute kidney injury score was lower in 5%ALB (9.5[6.5-10]) than 20%ALB (18[15-28.5], p = 0.0006) and RL (16 [15-19], p = 0.04). In lung tissue, mRNA expression of interleukin (IL)-6 was higher in RL (59.1[10.4-129.3]) than in 5%ALB (27.0[7.8-49.7], p = 0.04) or 20%ALB (3.7[7.8-49.7], p = 0.03), and IL-6 protein levels were higher in RL than 5%ALB and 20%ALB (p = 0.026 and p = 0.021, respectively). In kidney tissue, mRNA expression and protein levels of kidney injury molecule (KIM)-1 were lower in 5%ALB than RL and 20%ALB, while nephronectin expression increased (p = 0.01 and p = 0.01), respectively.

CONCLUSIONS

In a rat model of ALI, both iso-oncotic and hyper-oncotic albumin solutions were associated with less lung injury compared to Ringer's lactate. However, hyper-oncotic albumin resulted in greater kidney damage than iso-oncotic albumin. This experimental study is a step towards future clinical designs.

Assessing Extravascular Lung Water With Ultrasound: A Tool to Individualize Fluid Management?

Aggressive fluid resuscitation has become standard of care for hypotensive patients with sepsis. However, sepsis is a syndrome that occurs in patients with diverse underlying physiology and a one-size-fits-all approach to fluid administration seems misguided. To individualize fluid management, several methods to assess fluid responsiveness have been validated, but even in fluid responsive patients, fluid administration may still be harmful and lead to pulmonary edema. Hence, to individualize fluid management, in addition to fluid responsiveness, fluid tolerance needs to be assessed. This article examines whether lung ultrasound can be useful to detect excess extravascular lung water (EVLW) and thus assess fluid tolerance. The physiology of EVLW and the principles of lung ultrasound are briefly described. Articles examining the correlation between EVLW and lung ultrasound findings in various clinical settings are carefully reviewed. Overall, lung ultrasound has been found to be an excellent tool to detect EVLW, but large outcome studies investigating lung ultrasound-guided fluid management are still lacking.

A quantitative method for measuring the changes of lung surface wave speed for assessing disease progression of interstitial lung disease

Lung ultrasound surface wave elastography (LUSWE) is a novel non-invasive technique for measuring superficial lung tissue stiffness. The purpose of the study described here was to develop LUSWE for assessment of progression in patients with interstitial lung disease (ILD). In this study, LUSWE was used to measure changes in lung surface wave speeds at 100, 150 and 200 Hz through six intercostal lung spaces for 52 patients with ILD. The mean age was 63.1 ± 12.0 y (range: 20-85, 23 male and 29 female). The follow-up interval was 9.2 ± 3.5 mo depending on each patient's return appointment and availability. For each patient, disease progression between the baseline and follow-up tests was evaluated clinically using a 7-point Likert scale comprising three grades of improvement (mild, moderate, marked), unchanged status and three grades of worsening (mild, moderate, marked). Clinical assessments were based on changes in pulmonary function tests together with high-resolution computed tomography, echocardiography and clinical evaluations. This study illustrates the correlations between changes in lung surface wave speed and clinical assessments. Correlations of changes in lung surface wave speed at lower lateral and posterior portions of the lung portions with clinical assessments were good. LUSWE provides quantitative global and regional changes in lung surface wave speed that may be useful for quantitative assessment of progression of ILD.

Automatic Detection of B-Lines in In Vivo Lung Ultrasound

This paper proposes an automatic method for accurate detection and visualization of B-lines in ultrasound lung scans, which provides a quantitative measure for the number of B-lines present. All the scans used in this study were acquired using a BK3000 ultrasound scanner (BK Ultrasound, Herlev, Denmark) driving a 5.5-MHz linear transducer (BK Ultrasound). Four healthy subjects and four patients, after major surgery with pulmonary edema, were scanned at four locations on each lung for B-line examination. Eight sequences of 50 frames were acquired for each subject yielding 64 sequences in total. The proposed algorithm was applied to all 3200 in-vivo lung ultrasound images. The results showed that the average number of B-lines was 0.28±0.06 (Mean±Std) in scans belonging to the patients compared to 0.03 ± 0.06 (Mean ± Std) in the healthy subjects. Also, the Mann-Whitney test showed a significant difference between the two groups with the p -value of 0.015, and indicating that the proposed algorithm was able to differentiate between the healthy volunteers and the patients. In conclusion, the method can be used to automatically and to quantitatively characterize the distribution of B-lines for diagnosing pulmonary edema.

A Lung Phantom Model to Study Pulmonary Edema Using Lung Ultrasound Surface Wave Elastography

Lung ultrasound surface wave elastography (LUSWE) is a novel technique used to measure superficial lung tissue stiffness. A phantom study was carried out in the study described here to evaluate the application of LUSWE to assess lung water for pulmonary edema. A lung phantom model with cellulose sponge was used; various volumes of water were injected into the sponge to model lung water. Shaker-generated surface wave propagation on the sponge surface was recorded by a 10-MHz ultrasound probe at three shaker frequencies: 100, 150 and 200Hz. Surface wave speeds were calculated but did not exhibit dependence on the volume of injected water. However, the shear viscosity of the sponge increased with water content, and shear elasticity also exhibited a subtle increase. This study suggests that sponge viscoelasticity might change with the water content, which can be detected by LUSWE.

Visual assessment versus computer-assisted gray scale analysis in the ultrasound evaluation of neonatal respiratory status

Background and aim

Lung ultrasound has been used to describe common respiratory diseases both by visual and computer-assisted gray scale analysis. In the present paper, we compare both methods in assessing neonatal respiratory status keeping two oxygenation indexes as standards.

Patients and methods

Neonates admitted to the NICU for respiratory distress were enrolled. Two neonatologists not attending the patients performed a lung scan, built a single frame database and rated the images with a standardized score. The same dataset was processed using the gray scale analysis implemented with textural features and machine learning analysis. Both the oxygenation ratio (PaO2/FiO2) and the alveolar arterial oxygen gradient (A-a) were kept as reference standards.

Results

Seventy-five neonates with different respiratory status were enrolled in the study and a dataset of 600 ultrasound frames was built. Visual assessment of respiratory status correlated significantly with PaO2/FiO2 (r = -0.55; p<0.0001) and the A-a (r = 0.59; p<0.0001) with a strong interobserver agreement (K = 0.91). A significant correlation was also found between both oxygenation indexes and the gray scale analysis of lung ultrasound scans using regions of interest corresponding to 50K (r = -0.42; p<0.002 for PaO2/FiO2; r = 0.46 p<0.001 for A-a) and 100K (r = -0.35 p<0.01 for PaO2/FiO2; r = 0.58 p<0.0001 for A-a) pixels regions of interest.

Conclusions

A semi quantitative estimate of the degree of neonatal respiratory distress was demonstrated both by a validated scoring system and by computer assisted analysis of the ultrasound scan. This data may help to implement point of care ultrasound diagnostics in the NICU.