The role of ultrasound lung artifacts in the diagnosis of respiratory diseases

Use of Lung Ultrasound in Cystic Fibrosis: Is It a Valuable Tool?

Cystic fibrosis (CF) is a multisystem disorder characterized by progressive respiratory deterioration, significantly impacting both quality of life and survival. Over the years, lung ultrasound (LUS) has emerged as a promising tool in pediatric respiratory due to its safety profile and ease at the bedside. In the era of highly effective CF modulator therapies and improved life expectancy, the use of non-ionizing radiation techniques could become an integral part of CF management, particularly in the pediatric population. The present review explores the potential role of LUS in CF management based on available data, analyzing all publications from January 2015 to January 2024, focusing on two key areas: LUS in CF pulmonary exacerbation and its utility in routine clinical management. Nonetheless, LUS exhibits a robust correlation with computed tomography (CT) scans and serves as an additional, user-friendly imaging modality in CF management, demonstrating high specificity and sensitivity in identification, especially in consolidations and atelectasis in the CF population. Due to its ability, LUS could be an instrument to monitor exacerbations with consolidations and to establish therapy duration and monitor atelectasis over time or their evolution after therapeutic bronchoalveolar lavage. On the basis of our analysis, sufficient data emerged showing a good correlation between LUS score and respiratory function tests. Good sensitivity and specificity of the methodology have been found in rare CF pulmonary complications such as effusion and pneumothorax. Regarding its use in follow-up management, the literature reports a moderate correlation between LUS scores and the type, extent, and CT severity score of bronchiectasis. A future validation of ultrasound scores specifically in CF patients could improve the use of LUS to identify pulmonary exacerbations and monitor disease progression. However, further research is needed to comprehensively establish the role of LUS in the CF population, particularly in elucidating its broader utility and long-term impact on patient care.

Lung quantitative ultrasound to stage and monitor interstitial lung diseases

Chronic interstitial lung diseases (ILDs) require frequent point-of-care monitoring. X-ray-based methods lack resolution and are ionizing. Chest computerized tomographic (CT) scans are expensive and provide more radiation. Conventional ultrasound can detect severe lung damage via vertical artifacts (B-lines). However, this information is not quantitative, and the appearance of B-lines is operator- and system-dependent. Here we demonstrate novel ultrasound-based biomarkers to assess severity of ILDs. Lung alveoli scatter ultrasound waves, leading to a complex acoustic signature, which is affected by changes in alveolar density due to ILDs. We exploit ultrasound scattering in the lung and combine quantitative ultrasound (QUS) parameters, to develop ultrasound-based biomarkers that significantly correlate (p = 1e-4 for edema and p = 3e-7 for fibrosis) to the severity of pulmonary fibrosis and edema in rodent lungs. These innovative QUS biomarkers will be very significant for monitoring severity of chronic ILDs and response to treatment, especially in this new era of miniaturized and highly portable ultrasound devices.

Lessons from the pandemic and the value of a structured system of ultrasonographic findings in the diagnosis of COVID-19 pulmonary manifestations

Implementing a structured COVID-19 lung ultrasound system, using COVID-RADS standardization. This case series exams revealed correlations between ultrasonographic and tomographic findings. Ventilatory assessments showed that higher categories required second-line oxygen. This replicable tool will aid in screening and predicting disease severity beyond the pandemic.

OBJECTIVE

We aimed to share our experience in implementing a structured system for COVID-19 lung findings, elucidating key aspects of the lung ultrasound score to facilitate its standardized clinical use beyond the pandemic scenario.

METHODS

Using a scoring system to classify the extent of lung involvement, we retrospectively analyzed the ultrasound reports performed in our institution according to COVID-RADS standardization.

RESULTS

The study included 69 thoracic ultrasound exams, with 27 following the protocol. The majority of patients were female (52%), with ages ranging from 1 to 96 years and an average of 56 years. Classification according to COVID-RADS was as follows: 11.1% in category 0, 37% in category 1, 44.4% in category 2, and 7.4% in category 3. Ground-glass opacities on tomography correlated with higher COVID-RADS scores (categories 2 and 3) in 82% of cases. Ventilatory assessment revealed that 50% of cases in higher COVID-RADS categories (2 and 3) required second-line oxygen supplementation, while none of the cases in lower categories (0 and 1) utilized this support.

CONCLUSION

Lung ultrasound has been widely utilized as a diagnostic tool owing to its availability and simplicity of application. In the context of the pandemic emergency, a pressing need for a focused and easily applicable assessment arose. The structured reporting system, incorporating ultrasound findings for stratification, demonstrated ease of replicability. This system stands as a crucial tool for screening, predicting severity, and aiding in medical decisions, even in a non-pandemic context. Lung ultrasound enables precise diagnosis and ongoing monitoring of the disease. Ultrasound is an effective tool for assessing pulmonary findings in COVID-19. Structured reports enhance communication and are easily reproducible.

A lung disease diagnosis algorithm based on 2D spectral features of ultrasound RF signals

Lung diseases are commonly diagnosed based on clinical pathological indications criteria and radiological imaging tools (e.g., X-rays and CT). During a pandemic like COVID-19, the use of ultrasound imaging devices has broadened for emergency examinations by taking their unique advantages such as portability, real-time detection, easy operation and no radiation. This provides a rapid, safe, and cost-effective imaging modality for screening lung diseases. However, the current pulmonary ultrasound diagnosis mainly relies on the subjective assessments of sonographers, which has high requirements for the operator's professional ability and clinical experience. In this study, we proposed an objective and quantifiable algorithm for the diagnosis of lung diseases that utilizes two-dimensional (2D) spectral features of ultrasound radiofrequency (RF) signals. The ultrasound data samples consisted of a set of RF signal frames, which were collected by professional sonographers. In each case, a region of interest of uniform size was delineated along the pleural line. The standard deviation curve of the 2D spatial spectrum was calculated and smoothed. A linear fit was applied to the high-frequency segment of the processed data curve, and the slope of the fitted line was defined as the frequency spectrum standard deviation slope (FSSDS). Based on the current data, the method exhibited a superior diagnostic sensitivity of 98% and an accuracy of 91% for the identification of lung diseases. The area under the curve obtained by the current method exceeded the results obtained that interpreted by professional sonographers, which indicated that the current method could provide strong support for the clinical ultrasound diagnosis of lung diseases.

Deep learning for real-time multi-class segmentation of artefacts in lung ultrasound

Lung ultrasound (LUS) has emerged as a safe and cost-effective modality for assessing lung health, particularly during the COVID-19 pandemic. However, interpreting LUS images remains challenging due to its reliance on artefacts, leading to operator variability and limiting its practical uptake. To address this, we propose a deep learning pipeline for multi-class segmentation of objects (ribs, pleural line) and artefacts (A-lines, B-lines, B-line confluence) in ultrasound images of a lung training phantom. Lightweight models achieved a mean Dice Similarity Coefficient (DSC) of 0.74, requiring fewer than 500 training images. Applying this method in real-time, at up to 33.4 frames per second in inference, allows enhanced visualisation of these features in LUS images. This could be useful in providing LUS training and helping to address the skill gap. Moreover, the segmentation masks obtained from this model enable the development of explainable measures of disease severity, which have the potential to assist in the triage and management of patients. We suggest one such semi-quantitative measure called the B-line Artefact Score, which is related to the percentage of an intercostal space occupied by B-lines and in turn may be associated with the severity of a number of lung conditions. Moreover, we show how transfer learning could be used to train models for small datasets of clinical LUS images, identifying pathologies such as simple pleural effusions and lung consolidation with DSC values of 0.48 and 0.32 respectively. Finally, we demonstrate how such DL models could be translated into clinical practice, implementing the phantom model alongside a portable point-of-care ultrasound system, facilitating bedside assessment and improving the accessibility of LUS.

Lung Ultrasound Signs: The Beginning. Part 3-An Accademia di Ecografia Toracica Comprehensive Review on Ultrasonographic Signs and Real Needs

Over the last 20 years, scientific literature and interest on chest/lung ultrasound (LUS) have exponentially increased. Interpreting mixed-anatomical and artifactual-pictures determined the need of a proposal of a new nomenclature of artifacts and signs to simplify learning, spread, and implementation of this technique. The aim of this review is to collect and analyze different signs and artifacts reported in the history of chest ultrasound regarding normal lung, pleural pathologies, and lung consolidations. By reviewing the possible physical and anatomical interpretation of these artifacts and signs reported in the literature, this work aims to present the AdET (Accademia di Ecografia Toracica) proposal of nomenclature and to bring order between published studies.

Lung Quantitative Ultrasound to Stage and Monitor Interstitial Lung Diseases

Chronic interstitial lung diseases (ILDs) require frequent point-of-care monitoring. X-ray-based methods lack resolution and are ionizing. Chest computerized tomographic (CT) scans are expensive and provide more radiation. Conventional ultrasound can detect severe lung damage via vertical artifacts (B-lines). However, this information is not quantitative, and the appearance of B-lines is operator- and system-dependent. Here we demonstrate novel ultrasound-based biomarkers to assess severity of ILDs. Lung alveoli scatter ultrasound waves, leading to a complex acoustic signature, which is affected by changes in alveolar density due to ILDs. We exploit ultrasound scattering in the lung and combine Quantitative Ultrasound (QUS) parameters, to develop ultrasound-based biomarkers that significantly correlate to the severity of pulmonary fibrosis and edema in rodent lungs. These innovative QUS biomarkers will be very significant for monitoring severity of chronic ILDs and response to treatment, especially in this new era of miniaturized and highly portable ultrasound devices.

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

BACKGROUND

Newborns 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.

METHODS

Prospective 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.

RESULTS

The 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.

CONCLUSION

In CCHD, serial lung ultrasound (LUS) assessments, particularly in cases with increased PBF, can provide valuable guidance for managing patients during the perioperative period.

Parenchymal Cavitations in Pulmonary Tuberculosis: Comparison between Lung Ultrasound, Chest X-ray and Computed Tomography

This article aims to detect lung cavitations using lung ultrasound (LUS) in a cohort of patients with pulmonary tuberculosis (TB) and correlate the findings with chest computed tomography (CT) and chest X-ray (CXR) to obtain LUS diagnostic sensitivity. Patients with suspected TB were enrolled after being evaluated with CXR and chest CT. A blinded radiologist performed LUS within 3 days after admission at the Infectious Diseases Department. Finally, 82 patients were enrolled in this study. Bronchoalveolar lavage (BAL) confirmed TB in 58/82 (71%). Chest CT showed pulmonary cavitations in 38/82 (43.6%; 32 TB patients and 6 non-TB ones), LUS in 15/82 (18.3%; 11 TB patients and 4 non-TB ones) and CXR in 27/82 (33%; 23 TB patients and 4 non-TB ones). Twelve patients with multiple cavitations were detected with CT and only one with LUS. LUS sensitivity was 39.5%, specificity 100%, PPV 100% and NPV 65.7%. CXR sensitivity was 68.4% and specificity 97.8%. No false positive cases were found. LUS sensitivity was rather low, as many cavitated consolidations did not reach the pleural surface. Aerated cavitations could be detected with LUS with relative confidence, highlighting a thin air crescent sign towards the pleural surface within a hypoechoic area of consolidation, easily distinguishable from a dynamic or static air bronchogram.

Low-complexity lung ultrasound video scoring by means of intensity projection-based video compression

Since the outbreak of COVID-19, efforts have been made towards semi-quantitative analysis of lung ultrasound (LUS) data to assess the patient's condition. Several methods have been proposed in this regard, with a focus on frame-level analysis, which was then used to assess the condition at the video and prognostic levels. However, no extensive work has been done to analyze lung conditions directly at the video level. This study proposes a novel method for video-level scoring based on compression of LUS video data into a single image and automatic classification to assess patient's condition. The method utilizes maximum, mean, and minimum intensity projection-based compression of LUS video data over time. This enables to preserve hyper- and hypo-echoic data regions, while compressing the video down to a maximum of three images. The resulting images are then classified using a convolutional neural network (CNN). Finally, the worst predicted score given among the images is assigned to the corresponding video. The results show that this compression technique can achieve a promising agreement at the prognostic level (81.62%), while the video-level agreement remains comparable with the state-of-the-art (46.19%). Conclusively, the suggested method lays down the foundation for LUS video compression, shifting from frame-level to direct video-level analysis of LUS data.

COVID-19 Pediatric Follow-Up: Respiratory Long COVID-Associated Comorbidities and Lung Ultrasound Alterations in a Cohort of Italian Children

In children, the factors that influence COVID-19 disease and its medium- and long-term effects are little known. Our investigation sought to evaluate the presence of comorbidity factors associated with respiratory long COVID manifestations in children and to study ultrasound abnormalities following SARS-CoV-2 infection. Children, who arrived at the 'Respiratory Diseases of Pediatric Interest Unit' at the Department of Woman, Child, and General and Specialized Surgery of the University of Campania 'Luigi Vanvitelli', were selected during the timeframe from September 2021 to October 2022. The children were diagnosed with a SARS-CoV-2 infection that occurred at least one month before the visit. All patients followed a COVID-19 follow-up protocol, developed by the Italian Society of Pediatric Respiratory Diseases (SIMRI), which included: collection of data regarding SARS-CoV-2 illness and history of known respiratory and allergic diseases; physical examination; BMI assessment; baseline spirometry and after bronchodilation test; six-minute walking test; and lung ultrasound (LUS). In a cohort of 104 participants with respiratory long COVID symptoms (64.7% male, average age 8.92 years), 46.1% had fever with other symptoms, and 1% required hospitalization. BMI analysis showed 58.4% of the cohort was overweight. The LUS was positive in 27.0% of cases. A significant BMI association was observed with COVID-19 symptoms and LUS score (p-value < 0.05). No associations were found with asthma or atopy.

Prognostic Value of the Area of Lung Involved in Severe and Non-Severe Bronchiolitis: An Observational, Ultrasound-Based Study

BACKGROUND

Point of care lung ultrasound (LUS) has a definite role in viral bronchiolitis when combined with clinical data. Previous data showed a bigger involvement of the superior lung zones in more severe cases. The aim of the present study is to describe whether different lung areas are implicated to different degrees in patients admitted to a Pediatric Intensive Care Unit (PICU) and needing ventilation compared to those with less severe forms.

METHODS

observational, prospective study. LUS scores of single lung areas and clinical data were collected for all children aged 0-12 months presenting with bronchiolitis to the participating centers and used as covariates for logistic regression having "PICU admission" as outcome. A subsequent analysis was carried out to investigate factors concurring with different lung zones' involvement.

RESULTS

173 patients were enrolled. Difficulty in feeding, presence of wheezing, SpO2 were all risk factors for PICU admission. Superior lung areas' LUS scores presented higher Odds Ratios for PICU admission and need for ventilation than inferior ones. Age and prematurity concurred in determining their higher LUS scores.

CONCLUSIONS

Superior lobes' greater involvement could be favored by the geometrical distribution of relative bronchi, exiting with an acute angle from mainstem bronchi in small children where airway caliber is small and only small volumes of secretions can be occlusive.

The Role of Contrast-Enhanced Ultrasound Plus Color Parametric Imaging in the Differential Diagnosis of Subpleural Pulmonary Lesions

OBJECTIVES

To distinguish benign and malignant subpleural pulmonary lesions (SPLs) with contrast-enhanced ultrasound (CEUS) and color parametric imaging (CPI), and evaluate the role of CEUS plus CPI in the differential diagnosis of pathological types of SPLs.

METHODS

One hundred and thirty-six patients underwent CEUS with a Logiq E9 XD Clear ultrasonic machine equipped with a 3.5- to 5.0-MHz C5-1 transducer in our center were enrolled in our study, including 27 cases of benign lesions and 109 cases of malignant lesions. The ultrasound contrast agent used in this study was SonoVue. CEUS images and CPI of all cases were reviewed and analyzed by the resident and staff radiologist groups separately.

RESULTS

With CEUS alone, by both the two groups, the main enhancement pattern of benign SPLs was arborization (P < .001), while centripetal enhancement pattern occurred more frequently in malignant SPLs (P < .001). With CEUS plus CPI, by both the two groups, the main enhancement pattern of benign SPLs was arborization (P < .001), while those of malignant SPLs were centripetal (P < .001) and eccentric (P < .05). The diagnosis performance of CEUS plus CPI was significantly higher than that of CEUS alone in both the resident (area under the curve [AUC] = 0.857 vs 0.677, P < .001) and staff (AUC = 0.866 vs 0.681, P < .001) groups. Moreover, CPI offered remarkable inter-consistency improvements in the enhancement pattern determination between the two groups.

CONCLUSION

The CEUS enhancement patterns would provide information of blood perfusion patterns in the differential diagnosis of benign and malignant SPLs. The diagnosis performance could be significantly improved by CEUS plus CPI compared with CEUS alone.

Ultrasound multifrequency strategy to estimate the lung surface roughness, in silico and in vitro results

Lung ultrasound (LUS) is an important imaging modality to assess the state of the lung surface. Nevertheless, LUS is limited to the visual evaluation of imaging artifacts, especially the vertical ones. These artifacts are observed in pathologies characterized by a reduction of dimensions of air-spaces (alveoli). In contrast, there exist pathologies, such as chronic obstructive pulmonary disease (COPD), in which an enlargement of air-spaces can occur, which causes the lung surface to behave essentially as a perfect reflector, thus not allowing ultrasound penetration. This characteristic high reflectivity could be exploited to characterize the lung surface. Specifically, air-spaces of different sizes could cause the lung surface to have a different roughness, whose estimation could provide a way to assess the state of the lung surface. In this study, we present a quantitative multifrequency approach aiming at estimating the lung surface's roughness by measuring image intensity variations along the lung surface as a function of frequency. This approach was tested both in silico and in vitro, and it showed promising results. For the in vitro experiments, radiofrequency (RF) data were acquired from a novel experimental model. The results showed consistency between in silico and in vitro experiments.

Best Practice Recommendations for the Safe use of Lung Ultrasound

The safety of ultrasound is of particular importance when examining the lungs, due to specific bioeffects occurring at the alveolar air-tissue interface. Lung is significantly more sensitive than solid tissue to mechanical stress. The causal biological effects due to the total reflection of sound waves have also not been investigated comprehensively.On the other hand, the clinical benefit of lung ultrasound is outstanding. It has gained considerable importance during the pandemic, showing comparable diagnostic value with other radiological imaging modalities.Therefore, based on currently available literature, this work aims to determine possible effects caused by ultrasound on the lung parenchyma and evaluate existing recommendations for acoustic output power limits when performing lung sonography.This work recommends a stepwise approach to obtain clinically relevant images while ensuring lung ultrasound safety. A special focus was set on the safety of new ultrasound modalities, which had not yet been introduced at the time of previous recommendations.Finally, necessary research and training steps are recommended in order to close knowledge gaps in the field of lung ultrasound safety in the future.These recommendations for practice were prepared by ECMUS, the safety committee of the EFSUMB, with participation of international experts in the field of lung sonography and ultrasound bioeffects.

Point-of-Care Ultrasonography in Internal Medicine: Limitations and Pitfalls for Novice Users

Point-of-care ultrasound (POCUS) is increasingly being adopted in the field of internal medicine, leading to the development of POCUS curricula in undergraduate and postgraduate medical education programs. Prominent internal medicine societies and organizations worldwide recognize the expanding utilization of POCUS by internal medicine physicians, emphasizing the need for practitioners to be aware of both its benefits and limitations. Despite the growing enthusiasm for POCUS, clinicians, particularly those with limited clinical experience, must be cautious regarding its inherent limitations and the potential impact on their clinical practice. This review aims to outline the limitations and potential drawbacks of POCUS for medical students, residents, and internists who wish to stay abreast of the escalating use of POCUS in internal medicine and have a desire, or have already commenced, to incorporate POCUS into their practice. Additionally, it provides recommendations for enhancing POCUS proficiency to mitigate these limitations.

Ultrasound Scanning in Lung Procurement. Protocol for Decision-Making With the Purpose of Increasing Transplant Eligible Lungs

BACKGROUND

The main obstacle to obtaining lungs for transplantation is the shortage of donors. Once potential donors have been offered to transplant programs, the acceptance rate is highly variable, ranging from 5% to 20%. Minimizing donor leakage by converting potential lung donors into real donors is one of the key elements to improve results, and it is essential to have tools that facilitate decision-making in this scenario. The selection and rejection of transplantation-eligible lungs are usually made with chest x-rays; however, lung ultrasound scanning has shown better sensitivity and specificity for diagnosing pulmonary pathologies. Lung ultrasound scanning allows us to identify the reversible causes of low PaO2/fraction of inspired oxygen (FIO2) ratio, thus enabling the establishment of specific interventions, which, if proved successful, could turn lungs into transplant-eligible lungs. The available literature on its use in managing brain death donors and lung procurement is extremely scarce.

METHODS

A simple protocol aimed at identifying and treating the main reversible causes of low PaO2/FIO2 ratio to aid in decision-making is presented in this paper.

CONCLUSION

Lung ultrasound is a powerful, useful, and cheap technique available at the donor's bedside. It is conspicuously underused, despite being potentially helpful in decision-making by minimizing the discarding of donors, thus probably increasing the number of lungs sui for transplantation.

Benchmark methodological approach for the application of artificial intelligence to lung ultrasound data from COVID-19 patients: From frame to prognostic-level

Automated ultrasound imaging assessment of the effect of CoronaVirus disease 2019 (COVID-19) on lungs has been investigated in various studies using artificial intelligence-based (AI) methods. However, an extensive analysis of state-of-the-art Convolutional Neural Network-based (CNN) models for frame-level scoring, a comparative analysis of aggregation techniques for video-level scoring, together with a thorough evaluation of the capability of these methodologies to provide a clinically valuable prognostic-level score is yet missing within the literature. In addition to that, the impact on the analysis of the posterior probability assigned by the network to the predicted frames as well as the impact of temporal downsampling of LUS data are topics not yet extensively investigated. This paper takes on these challenges by providing a benchmark analysis of methods from frame to prognostic level. For frame-level scoring, state-of-the-art deep learning models are evaluated with additional analysis of best performing model in transfer-learning settings. A novel cross-correlation based aggregation technique is proposed for video and exam-level scoring. Results showed that ResNet-18, when trained from scratch, outperformed the existing methods with an F1-Score of 0.659. The proposed aggregation method resulted in 59.51%, 63.29%, and 84.90% agreement with clinicians at the video, exam, and prognostic levels, respectively; thus, demonstrating improved performances over the state of the art. It was also found that filtering frames based on the posterior probability shows higher impact on the LUS analysis in comparison to temporal downsampling. All of these analysis were conducted over the largest standardized and clinically validated LUS dataset from COVID-19 patients.

Machine Learning Algorithm Detection of Confluent B-Lines

OBJECTIVE

B-lines are a ring-down artifact of lung ultrasound that arise with increased alveolar water in conditions such as pulmonary edema and infectious pneumonitis. Confluent B-line presence may signify a different level of pathology compared with single B-lines. Existing algorithms aimed at B-line counting do not distinguish between single and confluent B-lines. The objective of this study was to test a machine learning algorithm for confluent B-line identification.

METHODS

This study used a subset of 416 clips from 157 subjects, previously acquired in a prospective study enrolling adults with shortness of breath at two academic medical centers, using a hand-held tablet and a 14-zone protocol. After exclusions, random sampling generated a total of 416 clips (146 curvilinear, 150 sector and 120 linear) for review. A group of five experts in point-of-care ultrasound blindly evaluated the clips for presence/absence of confluent B-lines. Ground truth was defined as majority agreement among the experts and used for comparison with the algorithm.

RESULTS

Confluent B-lines were present in 206 of 416 clips (49.5%). Sensitivity and specificity of confluent B-line detection by algorithm compared with expert determination were 83% (95% confidence interval [CI]: 0.77-0.88) and 92% (95% CI: 0.88-0.96). Sensitivity and specificity did not statistically differ between transducers. Agreement between algorithm and expert for confluent B-lines measured by unweighted κ was 0.75 (95% CI: 0.69-0.81) for the overall set.

CONCLUSION

The confluent B-line detection algorithm had high sensitivity and specificity for detection of confluent B-lines in lung ultrasound point-of-care clips, compared with expert determination.

Prevalence of diaphragm dysfunction in patients with interstitial lung disease (ILD): The role of diaphragmatic ultrasound

BACKGROUND

Diaphragm ultrasound (DUS) has been extensively used in critically ill patients while data on outpatients with interstitial lung disease (ILD) are limited. We hypothesized that diaphragm function, assessed by ultrasound, could be impaired in patients with ILD, considering both Idiopathic Pulmonary Fibrosis (IPF) and Connective Tissue Disease (CTD-ILD), compared to healthy subjects. Moreover, this impairment could impact clinical and functional parameters.

METHODS

All consecutive CTD-ILD and IPF patients followed in our center (March-October 2020) were screened. Diaphragm displacement (DD), inspiratory thickness (Ti), expiratory thickness (Te), thickening fraction (TF), and respiratory functional parameters were collected. The prevalence of diaphragmatic dysfunction (TF <30%) was then recorded.

RESULTS

Eighty-two consecutive patients (41 CTD-ILD, 41 IPF) and 15 age- and sex-matched controls were enrolled. In the overall population, 24 out of 82 (29%) presented diaphragmatic dysfunction. In CTD-ILD, DD and Ti were lower as compared to IPF (p = 0.021 and p = 0.036, respectively); while diaphragmatic dysfunction was more prevalent compared to controls (37% vs 7%, p = 0.043). TF positively correlated to patients' functional parameters in the CTD-ILD group (FVC%pred: p = 0.003; r = 0.45), while not in the IPF group. Diaphragmatic dysfunction was associated with moderate/severe dyspnea in both CTD-ILD and IPF (p = 0.021).

CONCLUSION

The prevalence of diaphragmatic dysfunction was 29% in patients with ILD and was associated with moderate/severe dyspnea. CTD-ILD presented lower DD compared with IPF and a higher prevalence of diaphragmatic dysfunction (TF<30%) compared with controls. TF was associated with lung function only in CTD-ILD patients, suggesting its potential role in the comprehensive patient assessment.

Lung Ultrasound Artifacts Interpreted as Pathology Footprints

Background: The original observation that lung ultrasound provides information regarding the physical state of the organ, rather than the anatomical details related to the disease, has reinforced the idea that the observed acoustic signs represent artifacts. However, the definition of artifact does not appear adequate since pulmonary ultrasound signs have shown valuable diagnostic accuracy, which has been usefully exploited by physicians in numerous pathologies. Method: A specific method has been used over the years to analyze lung ultrasound data and to convert artefactual information into anatomical information. Results: A physical explanation of the genesis of the acoustic signs is provided, and the relationship between their visual characteristics and the surface histopathology of the lung is illustrated. Two important sources of potential signal alteration are also highlighted. Conclusions: The acoustic signs are generated by acoustic traps that progressively release previously trapped energy. Consequently, the acoustic signs highlight the presence of acoustic traps and quantitatively describe their distribution on the lung surface; they are not artifacts, but pathology footprints and anatomical information. Moreover, the impact of the dynamic focusing algorithms and the impact of different probes on the visual aspect of the acoustic signs should not be neglected.

Use of Cardio-Pulmonary Ultrasound in the Neonatal Intensive Care Unit

Cardiopulmonary ultrasound (CPUS), the combination of lung ultrasound (LUS) and targeted neonatal echocardiography (TnECHO)AA, may offer a more appropriate approach to the challenging neonatal cardiovascular and respiratory disorders. This paper reviews the possible use of CPUS in the neonatal intensive care unit (NICU).

New International Guidelines and Consensus on the Use of Lung Ultrasound

Following the innovations and new discoveries of the last 10 years in the field of lung ultrasound (LUS), a multidisciplinary panel of international LUS experts from six countries and from different fields (clinical and technical) reviewed and updated the original international consensus for point-of-care LUS, dated 2012. As a result, a total of 20 statements have been produced. Each statement is complemented by guidelines and future developments proposals. The statements are furthermore classified based on their nature as technical (5), clinical (11), educational (3), and safety (1) statements.

Vertical Artifacts as Lung Ultrasound Signs: Trick or Trap? Part 2- An Accademia di Ecografia Toracica Position Paper on B-Lines and Sonographic Interstitial Syndrome

Although during the last few years the lung ultrasound (LUS) technique has progressed substantially, several artifacts, which are currently observed in clinical practice, still need a solid explanation of the physical phenomena involved in their origin. This is particularly true for vertical artifacts, conventionally known as B-lines, and for their use in clinical practice. A wider consensus and a deeper understanding of the nature of these artifactual phenomena will lead to a better classification and a shared nomenclature, and, ultimately, result in a more objective correlation between anatomo-pathological data and clinical scenarios. The objective of this review is to collect and document the different signs and artifacts described in the history of chest ultrasound, with a particular focus on vertical artifacts (B-lines) and sonographic interstitial syndrome (SIS). By reviewing the possible physical and anatomical interpretation of the signs and artifacts proposed in the literature, this work also aims to bring order to the available studies and to present the AdET (Accademia di Ecografia Toracica) viewpoint in terms of nomenclature and clinical approach to the SIS.

Lung ultrasound in the diagnosis of COVID-19-associated pneumonia

Over the past decades, lung ultrasound in the diagnosis of lung diseases has become widespread. Ultrasound examination has a number of advantages (no radiation exposure, real-time imaging, clear visualization of the subpleural lung regions and costophrenic angles), which make it possible to use ultrasound to monitor the dynamics of pneumonia in children and pregnant women. Currently, in the context of the COVID-19 pandemic, lung ultrasound is widely used due to its high diagnostic efficiency, which is comparable with classical radiography and X-ray computed tomography (CT) by a number of parameters.

The article describes the method of lung ultrasound and the radiographic pattern of COVID-19-associated pneumonia. It also provides a review of the literature, according to which the severity of pneumonia was determined, depending on the radiographic pattern, and the need for a lung ultrasound was identified.

The article indicates that information on assessment of the radiographic pattern of the lungs at runtime in different variants of the course of coronavirus infection, as well as many methodological issues, including the frequency of second-look lung ultrasound, has not been sufficiently studied.

Automatic detection of A-line in lung ultrasound images using deep learning and image processing

BACKGROUND

Auxiliary diagnosis and monitoring of lung diseases based on lung ultrasound (LUS) images is important clinical research. A-line is one of the most common indicators of LUS that can offer support for the assessment of lung diseases. A traditional A-line detection method mainly relies on experienced clinicians, which is inefficient and cannot meet the needs of these areas with backward medical level. Therefore, how to realize the automatic detection of A-line in LUS image is important.

PURPOSE

In order to solve the disadvantages of traditional A-line detection methods, realize automatic and accurate detection, and provide theoretical support for clinical application, we proposed a novel A-line detection method for LUS images with different probe types in this paper.

METHODS

First, the improved Faster R-CNN model with a selection strategy of localization box was designed to accurately locate the pleural line. Then, the LUS image below the pleural line was segmented for independent analysis excluding the influence of other similar structures. Next, image-processing methods based on total variation, matched filter, and gray difference were applied to achieve the automatic A-line detection. Finally, the "depth" index was designed to verify the accuracy by judging whether the automatic measurement results belong to corresponding manual results (±5%). In experiments, 3000 convex array LUS images were used for training and validating the improved pleural line localization model by five-fold cross validation. 850 convex array LUS images and 1080 linear array LUS images were used for testing the trained pleural line localization model and the proposed image-processing-based A-line detection method. The accuracy analysis, error statistics, and Harsdorff distance were employed to evaluate the experimental results.

RESULTS

After 100 epochs, the mean loss value of training and validation set of improved Faster R-CNN model reached 0.6540 and 0.7882, with the validation accuracy of 98.70%. The trained pleural line localization model was applied in the testing set of convex and linear probes and reached the accuracy of 97.88% and 97.11%, respectively, which were 3.83% and 8.70% higher than the original Faster R-CNN model. The accuracy, sensitivity, and specificity of A-line detection reached 95.41%, 0.9244%, 0.9875%, and 94.63%, 0.9230%, and 0.9766% for convex and linear probes, respectively. Compared to the experienced clinicians' results, the mean value and p value of depth error were 1.5342 ± 1.2097 and 0.9021, respectively, and the Harsdorff distance was 5.7305 ± 1.8311. In addition, the accumulated accuracy of the two-stage experiment (pleural line localization and A-line detection) was calculated as the final accuracy of the whole A-line detection system. They were 93.39% and 91.90% for convex and linear probes, respectively, which were higher than these previous methods.

CONCLUSIONS

The proposed method combining image processing and deep learning can automatically and accurately detect A-line in LUS images with different probe types, which has important application value for clinical diagnosis.

Pulmonary Effects of One Week of Repeated Recreational Closed-Circuit Rebreather Dives in Cold Water

Background and Objectives: The use of closed-circuit rebreathers (CCRs) in recreational diving is gaining interest. However, data regarding its physiological effects are still scarce. Immersion, cold water, hyperoxia, exercise or the equipment itself could challenge the cardiopulmonary system. The purpose of this study was to examine the impact of CCR diving on lung function and autonomous cardiac activity after a series of CCR dives in cold water. Materials and Methods: Eight CCR divers performed a diving trip (one week) in the Baltic Sea. Spirometry parameters, SpO₂, and the lung ultrasonography score (LUS) associated with hydration monitoring by bioelectrical impedance were assessed at the end of the week. Heart rate variability (HRV) was recorded during the dives. Results: No diver declared pulmonary symptoms. The LUS increased after dives combined with a slight non-pathological decrease in SpO₂. Spirometry was not altered, and all body water compartments were increased. Global HRV decreased during diving with a predominant increase in sympathetic tone while the parasympathetic tone decreased. All parameters returned to baseline 24 h after the last dive. Conclusions: The lung aeration disorders observed seem to be transient and not associated with functional spirometry alteration. The HRV dynamics highlighted physiological constraints during the dive as well as environmental-stress-related stimulation that may influence pulmonary changes. The impact of these impairments is unknown but should be taken into account, especially when considering long and repetitive CCR dives.

State of the Art in Lung Ultrasound, Shifting from Qualitative to Quantitative Analyses

Lung ultrasound (LUS) has been increasingly expanding since the 1990s, when the clinical relevance of vertical artifacts was first reported. However, the massive spread of LUS is only recent and is associated with the coronavirus disease 2019 (COVID-19) pandemic, during which semi-quantitative computer-aided techniques were proposed to automatically classify LUS data. In this review, we discuss the state of the art in LUS, from semi-quantitative image analysis approaches to quantitative techniques involving the analysis of radiofrequency data. We also discuss recent in vitro and in silico studies, as well as research on LUS safety. Finally, conclusions are drawn highlighting the potential future of LUS.

Lung Ultrasound Induction of Pulmonary Capillary Hemorrhage in Neonatal Swine

This study investigated induction of pulmonary capillary hemorrhage (PCH) in neonatal pigs (piglets) using three different machines: a GE Venue R1 point-of-care system with C1-5 and L4-12t probes, a GE Vivid 7 Dimension with a 7L probe and a SuperSonic Imagine machine with an SL15-4 probe and shear wave elastography (SWE). Female piglets were anesthetized, and each was mounted vertically in a warm bath for scanning at two or three intercostal spaces. After aiming at an innocuous output, the power was raised for a test exposure. Hydrophone measurements were used to calculate in situ values of mechanical index (MIIS). Inflated lungs were removed and scored for PCH area. For the C1-5 probe at 50% and 100% acoustical output (AO), a PCH threshold of 0.53 MIIS was obtained by linear regression (r2 = 0.42). The L4-12t probe did not induce PCH, but the 7L probe induced zones of PCH in the scan planes. The Venue R1 automated B-line tool applied with the C1-5 probe did not detect PCH induced by the C1-5 probe as B-line counts. However, when PCH induced by C1-5 and 7L exposures were subsequently scanned with the L4-12t probe using the automated tool, B-lines were counted in association with the PCH. The SWE induced PCH at push-pulse positions for 3, 30 and 300 s of SWE with PCH accumulating at 0.33 mm2/s and an exponential rise to a maximum of 18.4 mm2 (r2 = 0.61). This study demonstrated the induction of PCH by LUS of piglets, and supports the safety recommendation for use of MIs <0.4 in neonatal LUS.

Pleuropulmonary Ultrasound in Pediatrics: Proposal of a Reporting Model From the Academy of Thoracic Ultrasound

With the emergence of the Covid-19 pandemic, pleuropulmonary ultrasound has become a very common tool in clinical practice, even in the pediatric field. Therefore, the clinicians' need to speak a common ultrasound language becomes increasingly necessary. The Italian scientific society AdET (Academy of Thoracic Ultrasound) has been carrying out the study and dissemination of pulmonary ultrasound in medical practice in Italy for years. With this article, the pediatric AdET group wants to propose a report model of pediatric pulmonary ultrasound as a useful tool in daily clinical practice to interpret the images and reach a diagnostic conclusion, aiming to share a standardized approach that may also support the sharing of research findings.

Regional pleural strain measurements during mechanical ventilation using ultrasound elastography: A randomized, crossover, proof of concept physiologic study

Background

Mechanical ventilation is a common therapy in operating rooms and intensive care units. When ill-adapted, it can lead to ventilator-induced lung injury (VILI), which is associated with poor outcomes. Excessive regional pulmonary strain is thought to be a major mechanism responsible for VILI. Scarce bedside methods exist to measure regional pulmonary strain. We propose a novel way to measure regional pleural strain using ultrasound elastography. The objective of this study was to assess the feasibility and reliability of pleural strain measurement by ultrasound elastography and to determine if elastography parameters would correlate with varying tidal volumes.

Methods

A single-blind randomized crossover proof of concept study was conducted July to October 2017 at a tertiary care referral center. Ten patients requiring general anesthesia for elective surgery were recruited. After induction, patients received tidal volumes of 6, 8, 10, and 12 mL.kg^–1 in random order, while pleural ultrasound cineloops were acquired at 4 standardized locations. Ultrasound radiofrequency speckle tracking allowed computing various pleural translation, strain and shear components. We screened 6 elastography parameters (lateral translation, lateral absolute translation, lateral strain, lateral absolute strain, lateral absolute shear and Von Mises Strain) to identify those with the best dose-response with tidal volumes using linear mixed effect models. Goodness-of-fit was assessed by the coefficient of determination. Intraobserver, interobserver and test-retest reliability were calculated using intraclass correlation coefficients.

Results

Analysis was possible in 90.7% of ultrasound cineloops. Lateral absolute shear, lateral absolute strain and Von Mises strain varied significantly with tidal volume and offered the best dose-responses and data modeling fits. Point estimates for intraobserver reliability measures were excellent for all 3 parameters (0.94, 0.94, and 0.93, respectively). Point estimates for interobserver (0.84, 0.83, and 0.77, respectively) and test-retest (0.85, 0.82, and 0.76, respectively) reliability measures were good.

Conclusion

Strain imaging is feasible and reproducible. Future studies will have to investigate the clinical relevance of this novel imaging modality.

Clinical trial registration

www.Clinicaltrials.gov, identifier NCT03092557.

Pediatric COVID-19 Follow-Up with Lung Ultrasound: A Prospective Cohort Study

During the COVID-19 pandemic, lung ultrasound (LUS) was widely used to assess SARS-CoV-2 infection. To date, there are patients with persistence of symptoms after acute infection. Therefore, it may be useful to have an objective tool to follow these patients. The aim of our study was to evaluate the presence of LUS artifacts after SARS-CoV-2 infection in children and to analyze the associations between time elapsed since infection and symptomatology during acute infection. We conducted an observational study, enrolling 607 children infected with SARS-CoV-2 in the previous twelve months. All patients performed a LUS and medical history of demographic and clinical data. We observed irregular pleural lines in 27.5%, B-lines in 16.9%, and subpleural consolidations in 8.6% of the cases. These artifacts were more frequently observed in the lower lobe projections. We have observed that the frequency of artifacts decreases with increasing time since infection. In symptomatic patients during COVID infection, B-lines (p = 0.02) were more frequently found. In our sample, some children, even after months of acute infection, have ultrasound artifacts and showed an improvement with the passage of time from the acute episode. Our study provides additional evidence about LUS in children with previous COVID-19 as a support to follow these patients in the months following the infection.

Lung Ultrasound for Pleural Line Abnormalities, Confluent B-Lines, and Consolidation: Expert Reproducibility and a Method of Standardization

OBJECTIVES

Discrete B-lines have clear definitions, but confluent B-lines, consolidations, and pleural line abnormalities are less well defined. We proposed definitions for these and determined their reproducibility using COVID-19 patient images obtained with phased array probes.

METHODS

Two raters collaborated to refine definitions, analyzing disagreements on 107 derivation scans from 10 patients. Refined definitions were used by those raters and an independent rater on 1260 validation scans from 105 patients. Reliability was evaluated using intraclass correlation coefficients (ICC) or Cohen's kappa.

RESULTS

The agreement was excellent between collaborating raters for B-line abnormalities, ICC = 0.97 (95% confidence interval [CI] 0.97-0.98) and pleural line to consolidation abnormalities, ICC = 0.90 (95% CI 0.87-0.92). The independent rater's agreement for B-line abnormalities was excellent, ICC = 0.97 (95% CI 0.96-0.97) and for pleural line to consolidation was good, ICC = 0.88 (95% CI 0.84-0.91). Agreement just on pleural line abnormalities was weak (collaborators, κ = 0.54, 95% CI 0.48-0.60; independent, κ = 0.54, 95% CI 0.49-0.59).

CONCLUSION

With proposed definitions or via collaboration, overall agreement on confluent B-lines and pleural line to consolidation abnormalities was robust. Pleural line abnormality agreement itself was persistently weak and caution should be used interpreting pleural line abnormalities with only a phased array probe.

The Role of Lung Ultrasound in SARS-CoV-19 Pneumonia Management

Purpose: We aimed to assess the role of lung ultrasound (LUS) in the diagnosis and prognosis of SARS-CoV-2 pneumonia, by comparing it with High Resolution Computed Tomography (HRCT). Patients and methods: All consecutive patients with laboratory-confirmed SARS-CoV-2 infection and hospitalized in COVID Centers were enrolled. LUS and HRCT were carried out on all patients by expert operators within 48−72 h of admission. A four-level scoring system computed in 12 regions of the chest was used to categorize the ultrasound imaging, from 0 (absence of visible alterations with ultrasound) to 3 (large consolidation and cobbled pleural line). Likewise, a semi-quantitative scoring system was used for HRCT to estimate pulmonary involvement, from 0 (no involvement) to 5 (>75% involvement for each lobe). The total CT score was the sum of the individual lobar scores and ranged from 0 to 25. LUS scans were evaluated according to a dedicated scoring system. CT scans were assessed for typical findings of COVID-19 pneumonia (bilateral, multi-lobar lung infiltration, posterior peripheral ground glass opacities). Oxygen requirement and mortality were also recorded. Results: Ninety-nine patients were included in the study (male 68.7%, median age 71). 40.4% of patients required a Venturi mask and 25.3% required non-invasive ventilation (C-PAP/Bi-level). The overall mortality rate was 21.2% (median hospitalization 30 days). The median ultrasound thoracic score was 28 (IQR 20−36). For the CT evaluation, the mean score was 12.63 (SD 5.72), with most of the patients having LUS scores of 2 (59.6%). The bivariate correlation analysis displayed statistically significant and high positive correlations between both the CT and composite LUS scores and ventilation, lactates, COVID-19 phenotype, tachycardia, dyspnea, and mortality. Moreover, the most relevant and clinically important inverse proportionality in terms of P/F, i.e., a decrease in P/F levels, was indicative of higher LUS/CT scores. Inverse proportionality P/F levels and LUS and TC scores were evaluated by univariate analysis, with a P/F−TC score correlation coefficient of −0.762, p < 0.001, and a P/F−LUS score correlation coefficient of −0.689, p < 0.001. Conclusions: LUS and HRCT show a synergistic role in the diagnosis and disease severity evaluation of COVID-19.

Prognostic Role of Lung Ultrasound in Children with Bronchiolitis: Multicentric Prospective Study

There is increasing recognition of the role of lung ultrasound (LUS) to assess bronchiolitis severity in children. However, available studies are limited to small, single-center cohorts. We aimed to assess a qualitative and quantitative LUS protocol to evaluate the course of bronchiolitis at diagnosis and during follow-up. This is a prospective, multicenter study. Children with bronchiolitis were stratified according to clinical severity and underwent four LUS evaluations at set intervals. LUS was classified according to four models: (1) positive/negative; (2) main LUS pattern (normal/interstitial/consolidative/mixed) (3) LUS score; (4) LUS score with cutoff. Two hundred and thirty-three children were enrolled. The baseline LUS was significantly associated with bronchiolitis severity, using both the qualitative (positive/negative LUS p < 0.001; consolidated/normal LUS pattern or mixed/normal LUS p < 0.001) and quantitative models (cutoff score > 9 p < 0.001; LUS mean score p < 0.001). During follow-up, all LUS results according to all LUS models improved (p < 0.001). Better cut off value was declared at a value of >9 points. Conclusions: Our study supports the role of a comprehensive qualitative and quantitative LUS protocol for the identification of severe cases of bronchiolitis and provides data on the evolution of lung aeration during follow-up.

Effect of sigh in lateral position on postoperative atelectasis in adults assessed by lung ultrasound: a randomized, controlled trial

Background

Postoperative atelectasis occurs in 90% of patients receiving general anesthesia. Recruitment maneuvers (RMs) are not always effective and frequently associated with barotrauma and hemodynamic instability. It is reported that many natural physiological behaviors interrupted under general anesthesia could prevent atelectasis and restore lung aeration. This study aimed to find out whether a combined physiological recruitment maneuver (CPRM), sigh in lateral position, could reduce postoperative atelectasis using lung ultrasound (LUS).

Methods

We conducted a prospective, randomized, controlled trial in adults with open abdominal surgery under general anesthesia lasting for 2 h or longer. Subjects were randomly allocated to either control group (C-group) or CPRM-group and received volume-controlled ventilation with the same ventilator settings. Patients in CPRM group was ventilated in sequential lateral position, with the addition of periodic sighs to recruit the lung. LUS scores, dynamic compliance (Cdyn), the partial pressure of arterial oxygen (PaO₂) and fraction of inspired oxygen (FiO₂) ratio (PaO₂/FiO₂), and other explanatory variables were acquired from each patient before and after recruitment.

Results

Seventy patients were included in the analysis. Before recruitment, there was no significant difference in LUS scores, Cdyn and PaO₂/FiO₂ between CPRM-group and C-group. After recruitment, LUS scores in CPRM-group decreased significantly compared with C-group (6.00 [5.00, 7.00] vs. 8.00 [7.00, 9.00], p = 4.463e-11 < 0.05), while PaO₂/FiO₂ and Cdyn in CPRM-group increased significantly compared with C-group respectively (377.92 (93.73) vs. 309.19 (92.98), p = 0.008 < 0.05, and 52.00 [47.00, 60.00] vs. 47.70 [41.00, 59.50], p = 6.325e-07 < 0.05). No hemodynamic instability, detectable barotrauma or position-related complications were encountered.

Conclusions

Sigh in lateral position can effectively reduce postoperative atelectasis even without causing severe side effects. Further large-scale studies are necessary to evaluate it’s long-term effects on pulmonary complications and hospital length of stay.

Trial registration

ChiCTR1900024379. Registered 8 July 2019, 

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-022-01748-9.

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.

Diagnostic accuracy of lung ultrasound for transient tachypnea: a meta-analysis

OBJECTIVE

The objective of this meta-analysis was to study the diagnostic value of lung ultrasound (LUS) for transient tachypnea of the newborn (TTN).

METHODS

Embase, Cochrane Library, PubMed, Web of Science, and Google Scholar were searched, and the last search date was October 31, 2020. Studies on the diagnostic accuracy of pulmonary ultrasound for transient tachypnea were included. The quality assessment of the included study was assessed using the Diagnostic Accuracy Studies-2 tool. A meta-analysis was performed using Meta-Disc 1.4. A random-effects model was used and subgroup analysis was carried out to identify possible sources of heterogeneity.

RESULTS

A total of 378 articles were retrieved and nine studies with 3239 patients were included in the present meta-analysis. The overall quality of the included studies was moderate to high. The result of threshold analysis shows that there was no threshold effect. However, there was a significant heterogeneity caused by non-threshold effects in the included studies. A random-effects model was used. The pooled sensitivity, specificity, PLR and NLR were 0.55 (95% CI: 0.51-0.58), 0.98 (95% CI: 0.98-0.99), 58.30 (95% CI: 14.05-241.88) and 0.28 (95% CI: 0.18-0.43). The pooled DOR and AUC were 689.12 (95% CI: 68.71 to 6911.79) and 0.994. The results of subgroup analysis showed that the LUS diagnostic criteria and gold standard might be responsible for heterogeneity. Choosing "DLP combined with B line" as the diagnostic standard of LUS and choosing CXR as the gold standard could significantly improve the diagnostic performance of LUS.

CONCLUSION

LUS is a promising method to diagnose TTN. Only DLP is not enough to diagnose TTN, while DLP combined with B-line has good diagnostic performance.

The Value of Lung Ultrasound to Detect the Early Pleural and Pulmonary Pathologies in Nonhospitalized COVID-19-Suspected Cases in a Population With a Low Prevalence of COVID-19 Infection: A Prospective Study in 297 Subjects

OBJECTIVES

This prospective study aimed to evaluate the value of B-mode lung ultrasound (LUS) for the early diagnosis of coronavirus disease 2019 (COVID-19) infection in nonhospitalized COVID-19 suspected cases in a population with a low prevalence of disease.

METHODS

From April 2020 to June 2020, in an ambulatory testing center for COVID-19-suspected cases, 297 subjects were examined by LUS before a nasopharyngeal swab was taken for a reverse transcription polymerase chain reaction (RT-PCR) test. The following LUS findings were defined as pathological ultrasound findings and were analyzed: the presence of 1) pleural effusion, 2) B-lines, 3) fragmented visceral pleura, 4) consolidation, and 5) air bronchogram in the consolidation. The LUS findings were compared with the RT-PCR test results.

RESULTS

The result of the RT-PCR test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was positive in 11 and negative in 286 subjects, and the prevalence of COVID-19 infection in the study participants was 3.7%. On LUS, a pathological finding could be detected in 56/297 (18.9%) study participants. The LUS revealed a sensitivity of 27.3%, a specificity of 81.5%, a positive predictive value of 5.4%, a negative predictive value of 96.7%, and a diagnostic accuracy of 79.9% for the identification of COVID-19 infection.

CONCLUSIONS

For the identification of COVID-19 infection, LUS is highly sensitive to the patient spectrum and to the prevalence of the disease. Due to the low diagnostic performance in nonhospitalized COVID-19 cases in low-prevalence areas, LUS cannot be considered to be an adequate method for making a diagnosis in this group.

Lung Ultrasound Performed by Primary Care Physicians for Clinically Suspected Community-Acquired Pneumonia: A Multicenter Prospective Study

PURPOSE

We investigated whether lung ultrasound (US) performed in primary care is useful and feasible for diagnosing community-acquired pneumonia (CAP) compared with chest radiography, as most previous research has been conducted in hospital settings.

METHODS

We undertook a prospective observational cohort study of lung US performed in 12 primary care centers. Patients aged 5 years and older with symptoms suggesting CAP were examined with lung US (by 21 family physicians and 7 primary care pediatricians) and chest radiograph on the same day. We compared lung US findings with the radiologist's chest radiograph report as the reference standard, given that the latter is the most common imaging test performed for suspected CAP in primary care. The physicians had varied previous US experience, but all received a 5-hour lung US training program.

RESULTS

The study included 82 patients. Compared with chest radiography, positive lung US findings (consolidation measuring >1 cm or a focal/asymmetrical B-lines pattern) showed a sensitivity of 87.8%, a specificity of 58.5%, a positive likelihood-ratio of 2.12, and a negative likelihood-ratio of 0.21. Findings were similar regardless of the physicians' previous US training or experience. We propose a practical algorithm whereby patients having consolidation measuring greater than 1 cm or normal findings on lung US could skip chest radiography, whereas patients with a B-lines pattern without consolidation (given its low specificity) would need chest radiography to ensure appropriate management. Lung US was generally performed in 10 minutes or less.

CONCLUSION

Point-of-care lung US in primary care could be useful for investigating suspected CAP (avoiding chest radiography in most cases) and is likely feasible in daily practice, as short training programs appear sufficient and little time is needed to perform the scan.

Deep Learning-Based Classification of Reduced Lung Ultrasound Data From COVID-19 Patients

The application of lung ultrasound (LUS) imaging for the diagnosis of lung diseases has recently captured significant interest within the research community. With the ongoing COVID-19 pandemic, many efforts have been made to evaluate LUS data. A four-level scoring system has been introduced to semiquantitatively assess the state of the lung, classifying the patients. Various deep learning (DL) algorithms supported with clinical validations have been proposed to automate the stratification process. However, no work has been done to evaluate the impact on the automated decision by varying pixel resolution and bit depth, leading to the reduction in size of overall data. This article evaluates the performance of DL algorithm over LUS data with varying pixel and gray-level resolution. The algorithm is evaluated over a dataset of 448 LUS videos captured from 34 examinations of 20 patients. All videos are resampled by a factor of 2, 3, and 4 of original resolution, and quantized to 128, 64, and 32 levels, followed by score prediction. The results indicate that the automated scoring shows negligible variation in accuracy when it comes to the quantization of intensity levels only. Combined effect of intensity quantization with spatial down-sampling resulted in a prognostic agreement ranging from 73.5% to 82.3%.These results also suggest that such level of prognostic agreement can be achieved over evaluation of data reduced to 32 times of its original size. Thus, laying foundation to efficient processing of data in resource constrained environments.

Automated lung ultrasound scoring for evaluation of coronavirus disease 2019 pneumonia using two-stage cascaded deep learning model

Coronavirus disease 2019 (COVID-19) pneumonia has erupted worldwide, causing massive population deaths and huge economic losses. In clinic, lung ultrasound (LUS) plays an important role in the auxiliary diagnosis of COVID-19 pneumonia. However, the lack of medical resources leads to the low using efficiency of the LUS, to address this problem, a novel automated LUS scoring system for evaluating COVID-19 pneumonia based on the two-stage cascaded deep learning model was proposed in this paper. 18,330 LUS images collected from 26 COVID-19 pneumonia patients were successfully assigned scores by two experienced doctors according to the designed four-level scoring standard for training the model. At the first stage, we made a secondary selection of these scored images through five ResNet-50 models and five-fold cross validation to obtain the available 12,949 LUS images which were highly relevant to the initial scoring results. At the second stage, three deep learning models including ResNet-50, Vgg-19, and GoogLeNet were formed the cascaded scored model and trained using the new dataset, whose predictive result was obtained by the voting mechanism. In addition, 1000 LUS images collected another 5 COVID-19 pneumonia patients were employed to test the model. Experiments results showed that the automated LUS scoring model was evaluated in terms of accuracy, sensitivity, specificity, and F1-score, being 96.1%, 96.3%, 98.8%, and 96.1%, respectively. They proved the proposed two-stage cascaded deep learning model could automatically score an LUS image, which has great potential for application to the clinics on various occasions.

Radio-Histological Correlation of Lung Features in Severe COVID-19 Through CT-Scan and Lung Ultrasound Evaluation

Background

Patients with coronavirus disease 2019 (COVID-19) can develop severe bilateral pneumonia leading to respiratory failure. Lung histological samples were scarce due to the high risk of contamination during autopsies. We aimed to correlate histological COVID-19 features with radiological findings through lung ultrasound (LU)-guided postmortem core needle biopsies (CNBs) and computerized tomography (CT) scans.

Methodology

We performed an observational prospective study, including 30 consecutive patients with severe COVID-19. The thorax was divided into 12 explorations regions to correlate LU and CT-scan features. Histological findings were also related to radiological features through CNBs.

Results

Mean age was 62.56 ± 13.27 years old, with 96.7% male patients. Postmortem LU-guided CNBs were performed in 13 patients. Thirty patients were evaluated with both thoracic LU and chest CT scan, representing a total of 279 thoracic regions explored. The most frequent LU finding was B2-lines (49.1%). The most CT-scan finding was ground-glass opacity (GGO, 29%). Pathological CT-scan findings were commonly observed when B2-lines or C-lines were identified through LU (positive predictive value, PPV, 87.1%). Twenty-five postmortem echo-guided histological samples were obtained from 12 patients. Histological samples showed diffuse alveolar damage (DAD) (75%) and chronic interstitial inflammation (25%). The observed DAD was heterogeneous, showing multiple evolving patterns of damage, including exudative (33.3%), fibrotic (33.3%), and organizing (8.3%) phases. In those patients with acute or exudative pattern, two lesions were distinguished: classic hyaline membrane; fibrin "plug" in alveolar space (acute fibrinous organizing pneumonia, AFOP). C-profile was described in 33.3% and presented histological signs of DAD and lung fibrosis. The predominant findings were collagen deposition (50%) and AFOP (50%). B2-lines were identified in 66.7%; the presence of hyaline membrane was the predominant finding (37.5%), then organizing pneumonia (12.5%) and fibrosis (37.5%). No A-lines or B1-lines were observed in these patients.

Conclusion

LU B2-lines and C-profile are predominantly identified in patients with severe COVID-19 with respiratory worsening, which correspond to different CT patterns and histological findings of DAD and lung fibrosis.

Operative Use of Thoracic Ultrasound in Respiratory Medicine: A Clinical Study

For over 15 years, thoracic ultrasound has been applied in the evaluation of numerous lung diseases, demonstrating a variable diagnostic predictive power compared to traditional imaging techniques such as chest radiography and CT. However, in unselected pulmonary patients, there are no rigorous scientific demonstrations of the complementarity of thoracic ultrasound with traditional and standardized imaging techniques that use radiation. In this study 101 unselected pulmonary patients were evaluated blindly with ultrasound chest examinations during their hospital stay. Other instrumental examinations, carried out during hospitalization, were standard chest radiography, computed tomography (CT), and, when needed, radioisotopic investigation and cardiac catheterization. The operator who performed the ultrasound examinations was unaware of the anamnestic and clinical data of the patients. Diffuse fibrosing disease was detected with a sensitivity, specificity and diagnostic accuracy of 100%, 95% and 97%, respectively. In pleural effusions, ultrasound showed a sensitivity, specificity and diagnostic accuracy of 100%. In consolidations, the sensitivity, specificity and diagnostic accuracy were 83%, 98% and 93%, respectively. Low values of sensitivity were recorded for surface nodulations of less than one centimeter. Isolated subpleural ground glass densities were identified as White Lung with a sensitivity of 72% and a specificity of 86%. Only the associations Diffuse ultrasound findings/Definitive fibrosing disease, Ultrasound Consolidation/Definitive consolidation and non-diffuse ultrasound artefactual features/Definitive vascular pathology (pulmonary hypertension, embolism) were statistically significant with adjusted residuals of 7.9, 7 and 4.1, respectively. The obtained results show how chest ultrasound is an effective complementary diagnostic tool for the pulmonologist. When performed, as a complement to the patient’s physical examination, it can restrict the diagnostic hypothesis in the case of pleural effusion, consolidation and diffuse fibrosing disease of the lung.

What Is COVID 19 Teaching Us about Pulmonary Ultrasound?

In lung ultrasound (LUS), the interactions between the acoustic pulse and the lung surface (including the pleura and a small subpleural layer of tissue) are crucial. Variations of the peripheral lung density and the subpleural alveolar shape and its configuration are typically connected to the presence of ultrasound artifacts and consolidations. COVID-19 pneumonia can give rise to a variety of pathological pulmonary changes ranging from mild diffuse alveolar damage (DAD) to severe acute respiratory distress syndrome (ARDS), characterized by peripheral bilateral patchy lung involvement. These findings are well described in CT imaging and in anatomopathological cases. Ultrasound artifacts and consolidations are therefore expected signs in COVID-19 pneumonia because edema, DAD, lung hemorrhage, interstitial thickening, hyaline membranes, and infiltrative lung diseases when they arise in a subpleural position, generate ultrasound findings. This review analyzes the structure of the ultrasound images in the normal and pathological lung given our current knowledge, and the role of LUS in the diagnosis and monitoring of patients with COVID-19 lung involvement.

Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic

Ultrasound imaging of the lung has played an important role in managing patients with COVID-19-associated pneumonia and acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic, lung ultrasound (LUS) or point-of-care ultrasound (POCUS) has been a popular diagnostic tool due to its unique imaging capability and logistical advantages over chest X-ray and CT. Pneumonia/ARDS is associated with the sonographic appearances of pleural line irregularities and B-line artefacts, which are caused by interstitial thickening and inflammation, and increase in number with severity. Artificial intelligence (AI), particularly machine learning, is increasingly used as a critical tool that assists clinicians in LUS image reading and COVID-19 decision making. We conducted a systematic review from academic databases (PubMed and Google Scholar) and preprints on arXiv or TechRxiv of the state-of-the-art machine learning technologies for LUS images in COVID-19 diagnosis. Openly accessible LUS datasets are listed. Various machine learning architectures have been employed to evaluate LUS and showed high performance. This paper will summarize the current development of AI for COVID-19 management and the outlook for emerging trends of combining AI-based LUS with robotics, telehealth, and other techniques.

The Role of Lung Ultrasound in Low-Resource Settings During the Coronavirus (SARS-CoV-2) Pandemic

Lung ultrasound (LUS) has proven to be a helpful diagnostic tool for evaluating lung involvement in respiratory pathologies. The usage of this imaging technique became even more widespread during the SARS-CoV-2 pandemic. The latest generation ultrasound scanners are conveniently portable and this permits ultrasound examinations to be performed even in extreme environments where no other diagnostic tool is available. Our team has developed the first guide that assists the clinician while operating in low-resource settings, in managing a SARS-CoV-2 patient based on the clinical examination and the LUS findings.