Ultrasound findings of lung ultrasonography in COVID-19: A systematic review

Application and internal validation of lung ultrasound score in COVID-19 setting: The ECOVITA observational study

BACKGROUND

The severe acute respiratory syndrome Coronarovirus-2 associated still causes a significant number of deaths and hospitalizations mainly by the development of respiratory failure. We aim to validate lung ultrasound score in order to predict mortality and the severity of the clinical course related to the need of respiratory support.

METHODS

In this prospective multicenter hospital-based cohort study, all adult patients with diagnosis of SARS-CoV-2 infection, performed by real-time reverse transcription polymerase chain reaction were included. Upon admission, all patients underwent blood gas analysis and lung ultrasound by expert operators. The acquisition of ultrasound scan was performed on 12 peculiar anatomic landmarks of the chest. Lung ultrasound findings were classified according to a scoring method, ranging 0 to 3: Score 0: normal A-lines. Score 1: multiple separated B-lines. Score 2: coalescent B-lines, alteration of pleural line. Score 3: consolidation area.

RESULTS

One thousand and seven patients were included in statistical analysis (male 62.4 %, mean age 66.3). Oxygen support was needed in 811 (80.5 %) patients. The median ultrasound score was 24 and the risk of having more invasive respiratory support increased in relation to higher values score computed. Lung ultrasound score showed negative strong correlation (rho: -0.71) with the P/F ratio and a significant association with in-hospital mortality (OR 1.11, 95 %CI 1.07-1.14; p < 0.001), even after adjustment with the following variables (age, sex, P/F ratio, SpO2, lactate, hypertension, chronic renal failure, diabetes, and obesity).

CONCLUSIONS

The novelty of this research corroborates and validates the 12-field lung ultrasound score as tool for predicting mortality and severity clinical course in COVID-19 patients. Baseline lung ultrasound score was associated with in-hospital mortality and requirement of intensive respiratory support and predict the risk of IOT among COVID-19 patients.

The prognostic role of lung ultrasound in hospitalised patients with COVID-19. Correlation with chest CT findings and clinical markers of severity

BACKGROUND

The use of lung ultrasound (LUS) has recently become vital in the diagnosis and prognosis of various respiratory diseases. Its role in COVID-19 requires further investigation.

RESEARCH DESIGN AND METHODS

Twenty-five consecutive, non-ICU hospitalized COVID-19 patients were included. LUS was performed on admission and sequentially every 3 days at 8 points in the chest. Based on the LUS findings a score was designed. Logarithmic regression models and ROC curve analysis were applied.

RESULTS

A statistically significant positive correlation was found between LUS score at admission and the severity of SARS-COV-2 infection. Higher LUS score was significantly associated with lower PaO2/FiO2 ratio, use of HFNC, longer hospitalization and greater extent of chest CT infiltrates. A significant association between LUS score and risk of death or intubation or HFNC was found. For one point of increase in the score, risk of death or intubation or HFNC increased 1.93-fold (95% CI 1.02 to 3.65). The predictive role of the score was very satisfactory (area under the ROC curve = 0.87).

CONCLUSIONS

Lung ultrasound findings were significantly positively associated with clinical and radiological markers of severity of SARS-CoV-2 pneumonia. It therefore constitutes a promising and reliable technique for assessing pneumonia, comparable to chest CT.

Lung Ultrasound as a Tool for Analysis of Ventilation in Children With Respiratory Failure

OBJECTIVE

To describe lung ultrasound findings in children with respiratory failure on invasive mechanical ventilation (MV).

METHOD

This is a longitudinal, observational, quantitative study conducted in the pediatric intensive care unit. Children with respiratory distress syndrome, aged between 6 months and 7 years, on invasive MV were included in the study. Lung ultrasound was performed using the BLUE protocol in the first 48 h of hospitalization and during ventilatory weaning.

RESULTS

Seventeen patients who presented a significant reduction in A lines were included in the study. B lines showed worsening, indicating possible pulmonary complications. The decrease in pleural sliding from 14 to 3 (p = 0.04) and in the bat sign from 10 to 5 (p = 0.002) was statistically significant. The stratospheric sign showed a favorable trend (reduction from 3 to 0), but the p value of 0.08 was not significant. There was a moderate negative correlation between MV time and A lines, while a moderate positive correlation was observed between MV time and A lines. Furthermore, a moderate negative correlation between MV time and bat sign was also significant.

CONCLUSION

It is indicated that bedside lung ultrasound is a valuable tool for monitoring and management of children on invasive MV, allowing the follow-up of critical pediatric patients during the hospitalization period.

Pragmatic evaluation of point of care lung ultrasound for the triage of COVID-19 patients using a simple scoring matrix: Intraclass-classification and predictive value

BACKGROUND

The value of routine bedside lung ultrasound (LUS) for predicting patient disposition during visits to the Emergency Department (ED) is difficult to quantify. We hypothesized that a simplified scoring of bedside-acquired LUS images for the triage of acute respiratory symptoms in the ED would be associated with patient disposition.

METHODS

For this observational pragmatic study, we reviewed prospectively-collected bedside LUS images from patients presenting to the ED with acute respiratory symptoms. We agreed on a simplified LUS scoring approach (0-3). At least three reviewers blindly assessed the available LUS images for each patient and determined the worst score for each patient and the presence of individual LUS findings. The worst LUS score was used to classify patients' LUS-suggested hospital admission risk. We evaluated the agreement between reviewers and the predictive value of LUS findings for patient disposition.

RESULTS

204 patients were eligible, and 126 sets of images were available and scored. The most common LUS finding were isolated B-lines (63.5 % of LUS images), pleural thickening/irregularity (48.4 %), and diffuse B-lines (43.7 %). The patients' worst LUS score were 2 (43.5 %), 3 (26.1 %), 1 (20.7 %), and 0 (9.8 %). There was good agreement among reviewers on the worst LUS score (intra-class correlation coefficient 0.830, 95 % confidence interval (0.772-0.875)) and the LUS-suggested disposition (ICC 0.882, 95 % CI (0.846, 0.911)).

CONCLUSION

A simplified scoring of bedside-acquired LUS images from patients with acute respiratory symptoms at the emergency department reliably predicts patient disposition.

Clinical and ultrasound characteristics in patients with sars-cov-2 pneumonia, associated with hospitalization prognosis. e-covid project

BACKGROUND

During the COVID-19 pandemia, the imaging test of choice to diagnose COVID-19 pneumonia as chest computed tomography (CT). However, access was limited in the hospital setting and patients treated in Primary Care (PC) could only access the chest x-ray as an imaging test. Several scientific articles that demonstrated the sensitivity of lung ultrasound, being superior to chest x-ray [Cleverley J et al., BMJ 370, 202013] and comparable to CT scan [Tung-Chen Y et al., Ultrasound Med Biol 46:2918-2926, 2020], promoted the incorporation of this technique in the assessment of COVID-19 patients in PC. [Pérez J et al., Arch. Bronconeumol 56:27-30, 2020; Gargani L et al., Eur Heart J Cardiovasc Imaging 21:941-8, 2020, Soldati G et al., J Ultrasound Med 39:1459, 2020] A prior study in our territory (Lleida, Spain) was designed to predict complications (hospital admission) of COVID-19 pneumonia in PC patients, being different patterns of Lung ultrasounds (LUS) risk factors for hospital admission. [Martínez Redondo J et al., Int J Environ Res Public Health 18:3481, 2021] The rationale for conducting this study lies in the urgent need to understand the determinants of severity and prognosis in COVID-19 patients with interstitial pneumonia, according to its lung ultrasound patterns. This research is crucial to provide a deeper understanding of how these pre-existing ultrasound patterns related to disease progression influence the medical treatment.

METHODS

The objective of the study is to generate predictive models of lung ultrasound patterns for the prediction of lung areas characteristics associated with hospitalizations and admissions to the Intensive Care Unit (ICU) associated with COVID-19 disease, using ultrasound, sociodemographic and medical data obtained through the computerized medical history.

RESULTS

A single relevant variable has been found for the prediction of hospitalization (number of total regions with potentially pathological presence of B lines) and one for the prediction of ICU admission (number of regions of the right lung with potentially pathological presence of B lines). In both cases it has been determined that the optimal point for classification was 2 or more lung affected areas. Those areas under the curve have been obtained with good predictive capacity and consistency in both cohorts.

CONCLUSIONS

The results of this study will contribute to the determination of the ultrasound prognostic value based on the number of lung areas affected, the presence of pulmonary condensation or the irregularity of pleural effusion patterns in COVID-19 patients, being able to be extended to other lung viral infections with similar patterns.

Airway and Thoracic Ultrasound

Airway and thoracic ultrasound applications can provide critical information to improve patient safety for procedures and management of pulmonary conditions. Emergency physicians should utilize airway ultrasound in the preparation for an anatomically and/or physiologically difficult airway, which may include site demarcation for surgical airway planning. Thoracic ultrasound is useful in the prompt evaluation of a dyspneic patient. This article underscores the crucial role of airway and thoracic ultrasound in emergency medicine, emphasizing its utility for assessing difficult airways, planning surgical airways, and promptly evaluating dyspneic patients.

Conventional and Contrast-enhanced US of the Lung: From Performance to Diagnosis

In recent years, lung US has evolved from a marginal tool to an integral component of diagnostic chest imaging. Contrast-enhanced US (CEUS) can improve routine gray-scale imaging of the lung and chest, particularly in diagnosis of peripheral lung diseases (PLDs). Although an underused tool in many centers, and despite inherent limitations in evaluation of central lung disease caused by high acoustic impedance between air and soft tissues, lung CEUS has emerged as a valuable tool in diagnosis of PLDs. Owing to the dual arterial supply to the lungs via pulmonary and bronchial (systemic) arteries, different enhancement patterns can be observed at lung CEUS, thereby enabling accurate differential diagnoses in various PLDs. Lung CEUS also assists in identifying patients who may benefit from complementary diagnostic tests, including image-guided percutaneous biopsy. Moreover, lung CEUS-guided percutaneous biopsy has shown feasibility in accessible subpleural lesions, enabling higher histopathologic performance without significantly increasing either imaging time or expenses compared with conventional US. The authors discuss the technique of and basic normal and pathologic findings at conventional lung US, followed by a more detailed discussion of lung CEUS applications, emphasizing specific aspects of pulmonary physiology, basic concepts in lung US enhancement, and the most commonly encountered enhancement patterns of different PLDs. Finally, they discuss the benefits of lung CEUS in planning and guidance of US-guided lung biopsy. ©RSNA, 2024 Supplemental material is available for this article.

Deep learning models for interpretation of point of care ultrasound in military working dogs

Introduction

Military working dogs (MWDs) are essential for military operations in a wide range of missions. With this pivotal role, MWDs can become casualties requiring specialized veterinary care that may not always be available far forward on the battlefield. Some injuries such as pneumothorax, hemothorax, or abdominal hemorrhage can be diagnosed using point of care ultrasound (POCUS) such as the Global FAST® exam. This presents a unique opportunity for artificial intelligence (AI) to aid in the interpretation of ultrasound images. In this article, deep learning classification neural networks were developed for POCUS assessment in MWDs.

Methods

Images were collected in five MWDs under general anesthesia or deep sedation for all scan points in the Global FAST® exam. For representative injuries, a cadaver model was used from which positive and negative injury images were captured. A total of 327 ultrasound clips were captured and split across scan points for training three different AI network architectures: MobileNetV2, DarkNet-19, and ShrapML. Gradient class activation mapping (GradCAM) overlays were generated for representative images to better explain AI predictions.

Results

Performance of AI models reached over 82% accuracy for all scan points. The model with the highest performance was trained with the MobileNetV2 network for the cystocolic scan point achieving 99.8% accuracy. Across all trained networks the diaphragmatic hepatorenal scan point had the best overall performance. However, GradCAM overlays showed that the models with highest accuracy, like MobileNetV2, were not always identifying relevant features. Conversely, the GradCAM heatmaps for ShrapML show general agreement with regions most indicative of fluid accumulation.

Discussion

Overall, the AI models developed can automate POCUS predictions in MWDs. Preliminarily, ShrapML had the strongest performance and prediction rate paired with accurately tracking fluid accumulation sites, making it the most suitable option for eventual real-time deployment with ultrasound systems. Further integration of this technology with imaging technologies will expand use of POCUS-based triage of MWDs.

Lung Ultrasonography in the Evaluation of Late Sequelae of COVID-19 Pneumonia-A Comparison with Chest Computed Tomography: A Prospective Study

The onset of the COVID-19 pandemic allowed physicians to gain experience in lung ultrasound (LUS) during the acute phase of the disease. However, limited data are available on LUS findings during the recovery phase. The aim of this study was to evaluate the utility of LUS to assess lung involvement in patients with post-COVID-19 syndrome. This study prospectively enrolled 72 patients who underwent paired LUS and chest CT scans (112 pairs including follow-up). The most frequent CT findings were ground glass opacities (83.3%), subpleural lines (72.2%), traction bronchiectasis (37.5%), and consolidations (31.9%). LUS revealed irregular pleural lines as a common abnormality initially (56.9%), along with subpleural consolidation >2.5 mm ≤10 mm (26.5%) and B-lines (26.5%). A strong correlation was found between LUS score, calculated by artificial intelligence percentage involvement in ground glass opacities described in CT (r = 0.702, p < 0.05). LUS score was significantly higher in the group with fibrotic changes compared to the non-fibrotic group with a mean value of 19.4 ± 5.7 to 11 ± 6.6, respectively (p < 0.0001). LUS might be considered valuable for examining patients with persistent symptoms after recovering from COVID-19 pneumonia. Abnormalities identified through LUS align with CT scan findings; thus, LUS might potentially reduce the need for frequent chest CT examinations.

Evaluation of a Lung Ultrasound Score in Hospitalized Adult Patients with COVID-19 in Barcelona, Spain

Background/Objectives: During the COVID-19 pandemic and the burden on hospital resources, the rapid categorization of high-risk COVID-19 patients became essential, and lung ultrasound (LUS) emerged as an alternative to chest computed tomography, offering speed, non-ionizing, repeatable, and bedside assessments. Various LUS score systems have been used, yet there is no consensus on an optimal severity cut-off. We assessed the performance of a 12-zone LUS score to identify adult COVID-19 patients with severe lung involvement using oxygen saturation (SpO2)/fractional inspired oxygen (FiO2) ratio as a reference standard to define the best cut-off for predicting adverse outcomes. Methods: We conducted a single-centre prospective study (August 2020-April 2021) at Hospital del Mar, Barcelona, Spain. Upon admission to the general ward or intensive care unit (ICU), clinicians performed LUS in adult patients with confirmed COVID-19 pneumonia. Severe lung involvement was defined as a SpO2/FiO2 ratio <315. The LUS score ranged from 0 to 36 based on the aeration patterns. Results: 248 patients were included. The admission LUS score showed moderate performance in identifying a SpO2/FiO2 ratio <315 (area under the ROC curve: 0.71; 95%CI 0.64-0.77). After adjustment for COVID-19 risk factors, an admission LUS score ≥17 was associated with an increased risk of in-hospital death (OR 5.31; 95%CI: 1.38-20.4), ICU admission (OR 3.50; 95%CI: 1.37-8.94) and need for IMV (OR 3.31; 95%CI: 1.19-9.13). Conclusions: Although the admission LUS score had limited performance in identifying severe lung involvement, a cut-off ≥17 score was associated with an increased risk of adverse outcomes. and could play a role in the rapid categorization of COVID-19 pneumonia patients, anticipating the need for advanced care.

Transfer Learning-Based B-Line Assessment of Lung Ultrasound for Acute Heart Failure

OBJECTIVE

B-lines assessed by lung ultrasound (LUS) outperform physical exam, chest radiograph, and biomarkers for the associated diagnosis of acute heart failure (AHF) in the emergent setting. The use of LUS is however limited to trained professionals and suffers from interpretation variability. The objective was to utilize transfer learning to create an AI-enabled software that can aid novice users to automate LUS B-line interpretation.

METHODS

Data from an observational AHF LUS study provided standardized cine clips for AI model development and evaluation. A total of 49,952 LUS frames from 30 patients were hand scored and trained on a convolutional neural network (CNN) to interpret B-lines at the frame level. A random independent evaluation set of 476 LUS clips from 60 unique patients assessed model performance. The AI models scored the clips on both a binary and ordinal 0-4 multiclass assessment.

RESULTS

A multiclassification AI algorithm had the best performance at the binary level when applied to the independent evaluation set, AUC of 0.967 (95% CI 0.965-0.970) for detecting pathologic conditions. When compared to expert blinded reviewer, the 0-4 multiclassification AI algorithm scale had a reported linear weighted kappa of 0.839 (95% CI 0.804-0.871).

CONCLUSIONS

The multiclassification AI algorithm is a robust and well performing model at both binary and ordinal multiclass B-line evaluation. This algorithm has the potential to be integrated into clinical workflows to assist users with quantitative and objective B-line assessment for evaluation of AHF.

Comparison of lung ultrasound scoring systems for the prognosis of COVID-19 in the emergency department: An international prospective cohort study

Purpose

The purpose of this study was to evaluate whether the lung ultrasound (LUS) scores applied to an international cohort of patients presenting to the emergency department (ED) with suspected COVID-19, and subsequently admitted with proven disease, could prognosticate clinical outcomes.

Methods

This was an international, multicentre, prospective, observational cohort study of patients who received LUS and were followed for the composite primary outcome of intubation, intensive care unit (ICU) admission or death. LUS scores were later applied including two 12-zone protocols ('de Alencar score' and 'CLUE score'), a 12-zone protocol with lung and pleural findings ('Ji score') and an 11-zone protocol ('Tung-Chen score'). The primary analysis comprised logistic regression modelling of the composite primary outcome, with the LUS scores analysed individually as predictor variables.

Results

Between April 2020 to April 2022, 129 patients with COVID-19 had LUS performed according to the protocol and 24 (18.6%) met the composite primary endpoint. No association was seen between the LUS score and the composite primary end point for the de Alencar score [odds ratio (OR) = 1.04; 95% confidence interval (CI): 0.97-1.11; P = 0.29], the CLUE score (OR = 1.03; 95% CI: 0.96-1.10; P = 0.40), the Ji score (OR = 1.02; 95% CI: 0.97-1.07; P = 0.40) or the Tung-Chen score (OR = 1.02; 95% CI: 0.97-1.08).

Discussion

Compared to these earlier studies performed at the start of the pandemic, the negative outcome of our study could reflect the changing scenario of the COVID-19 pandemic, including patient, disease, and system factors. The analysis suggests that the study may have been underpowered to detect a weaker association between a LUS score and the primary outcome.

Conclusion

In an international cohort of adult patients presenting to the ED with suspected COVID-19 disease who had LUS performed and were subsequently admitted to hospital, LUS severity scores did not prognosticate the need for invasive ventilation, ICU admission or death.

A Randomised Control Study Comparing Ultrasonography with Standard Clinical Methods in Assessing Endotracheal Tube Tip Positioning

Introduction

Airway ultrasound has been increasingly used in correct positioning of endotracheal tube. We hypothesize that a safe distance between endotracheal tube tip and carina can be achieved with the aid of ultrasound.

Aim of the study

Our primary objective was to determine whether ultrasound guided visualisation of proximal end of endotracheal tube cuff is better when compared to conventional method in optimal positioning of tube tip. The secondary objective was to find the optimal endotracheal tube position at the level of incisors in adult Indian population.

Materials and Methods

There were 25 patients each in the conventional group and the ultrasound group. Conventional method includes auscultation and end tidal capnography. In the ultrasound group the upper end of the endotracheal tube cuff was positioned with an intent to provide 4 cm distance from the tube tip to the carina. X ray was used in both groups for confirmation of tip position and comparison between the two groups. Further repositioning of the tube was done if indicated and the mean length of the tube at incisors was then measured.

Results

After x ray confirmation, endotracheal tube repositioning was required in 24% of patients in the USG group and 40 % of patients in the conventional group. However, this result was not found to be statistically significant (p = 0.364). The endotracheal tube length at the level of teeth was 19.4 ± 1.35 cm among females and 20.95 ± 1.37 cm among males.

Conclusions

Ultrasonography is a reliable method to determine ETT position in the trachea. There was no statistically significant difference when compared to the conventional method. The average length of ETT at the level of incisors was 19.5 cm for females and 21 cm for males.

Coupling speckle noise suppression with image classification for deep-learning-aided ultrasound diagnosis

Objective. During deep-learning-aided (DL-aided) ultrasound (US) diagnosis, US image classification is a foundational task. Due to the existence of serious speckle noise in US images, the performance of DL models may be degraded. Pre-denoising US images before their use in DL models is usually a logical choice. However, our investigation suggests that pre-speckle-denoising is not consistently advantageous. Furthermore, due to the decoupling of speckle denoising from the subsequent DL classification, investing intensive time in parameter tuning is inevitable to attain the optimal denoising parameters for various datasets and DL models. Pre-denoising will also add extra complexity to the classification task and make it no longer end-to-end.Approach. In this work, we propose a multi-scale high-frequency-based feature augmentation (MSHFFA) module that couples feature augmentation and speckle noise suppression with specific DL models, preserving an end-to-end fashion. In MSHFFA, the input US image is first decomposed to multi-scale low-frequency and high-frequency components (LFC and HFC) with discrete wavelet transform. Then, multi-scale augmentation maps are obtained by computing the correlation between LFC and HFC. Last, the original DL model features are augmented with multi-scale augmentation maps.Main results. On two public US datasets, all six renowned DL models exhibited enhanced F1-scores compared with their original versions (by 1.31%-8.17% on the POCUS dataset and 0.46%-3.89% on the BLU dataset) after using the MSHFFA module, with only approximately 1% increase in model parameter count.Significance. The proposed MSHFFA has broad applicability and commendable efficiency and thus can be used to enhance the performance of DL-aided US diagnosis. The codes are available athttps://github.com/ResonWang/MSHFFA.

Tiny Lungs, Big Differences: Navigating the Varied COVID-19 Landscape in Neonates vs. Infants via Biomarkers and Lung Ultrasound

Due to their susceptibilities, neonates and infants face unique SARS-CoV-2 challenges. This retrospective study will compare the illness course, symptoms, biomarkers, and lung damage in neonates and infants with SARS-CoV-2 infection from February 2020 to October 2023. This study was conducted at two hospitals in Timisoara, Romania, using real-time multiplex PCR to diagnose and lung ultrasonography (LUS) to assess lung involvement. Neonates had a more severe clinical presentation, an increased immune response, and greater lung involvement. Neonates had more PCR-positive tests (p = 0.0089) and longer hospital stays (p = 0.0002). In neonates, LDH, CRP, and ferritin levels were higher, indicating a stronger inflammatory response. Reduced oxygen saturation in neonates indicates respiratory dysfunction. The symptoms were varied. Infants had fever, cough, and rhinorrhea, while neonates had psychomotor agitation, acute dehydration syndrome, and candidiasis. This study emphasizes individualized care and close monitoring for neonatal SARS-CoV-2 infections. Newborn lung ultrasonography showed different variances and severity levels, emphasizing the need for targeted surveillance and therapy. Newborns have high lung ultrasound scores (LUSS), indicating significant lung involvement. Both groups had initial lung involvement, but understanding these modest differences is crucial to improving care for these vulnerable populations.

Lung, Pleura, and Diaphragm Point-of-Care Ultrasound

Thoracic Ultrasonography involves the ultrasonographic examination of the lungs, pleura, and diaphragm. This provides a plethora of clinical information during the point of care assessment of patients. The air filled lungs create consistent artifacts and careful examination and understanding of these artefactual signs can provide useful information on underlying clinicopathologic states. This review aims to provide a review of the ultrasound signs and features that can be seen in horacic ultrasonography and summarize the clinical evidence to support its use.

Point-of-care ultrasound use in COVID-19: a narrative review

Background and Objective

The coronavirus disease 2019 (COVID-19) pandemic that began in early 2020 resulted in significant mortality from respiratory tract infections. Existing imaging modalities such as chest X-ray (CXR) lacks sensitivity in its diagnosis while computed tomography (CT) scan carries risks of radiation and contamination. Point-of-care ultrasound (POCUS) has the advantage of bedside testing with higher diagnostic accuracy. We aim to describe the various applications of POCUS for patients with suspected severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in the emergency department (ED) and intensive care unit (ICU).

Methods

We performed literature search on the use of POCUS in the diagnosis and management of COVID-19 in MEDLINE, Embase and Scopus databases using the following search terms: "ultrasonography", "ultrasound", "COVID-19", "SARS-CoV-2", "SARS-CoV-2 variants", "emergency services", "emergency department" and "intensive care units". Search was performed independently by two reviewers with any discrepancy adjudicated by a third member.

Key Content and Findings

Lung POCUS in patients with COVID-19 shows different ultrasonographic features from pulmonary oedema, bacterial pneumonia, and other viral pneumonia, thus useful in differentiating between these conditions. It is more sensitive than CXR, and more accessible and widely available than CT scan. POCUS can be used to diagnose COVID-19 pneumonia, screen for COVID-19-related pulmonary and extrapulmonary complications, and guide management of ICU patients, such as timing of ventilator weaning based on lung POCUS findings.

Conclusions

POCUS is a useful and rapid point-of-care modality that can be used to aid in diagnosis, management, and risk stratification of COVID-19 patients in different healthcare settings.

Lung ultrasound score and in-hospital mortality of adults with acute respiratory distress syndrome: a meta-analysis

BACKGROUND

Lung ultrasound (LUS) score could quantitatively reflect the lung aeration, which has been well applied in critically ill patients. The aim of the systematic review and meta-analysis was to evaluate the association between LUS score at admission and the risk of in-hospital mortality of adults with acute respiratory distress syndrome (ARDS).

METHODS

Toachieve the objective of this meta-analysis, we conducted a thorough search of PubMed, Embase, Cochrane Library, and the Web of Science to identify relevant observational studies with longitudinal follow-up. We employed random-effects models to combine the outcomes, considering the potential influence of heterogeneity.

RESULTS

Thirteen cohort studies with 1,022 hospitalized patients with ARDS were included. Among them, 343 patients (33.6%) died during hospitalization. The pooled results suggested that the LUS score at admission was higher in non-survivors as compared to survivors (standardized mean difference = 0.73, 95% confidence interval [CI]: 0.55 to 0.91, p < 0.001; I2 = 25%). Moreover, a high LUS score at admission was associated with a higher risk of in-hospital mortality of patients with ARDS (risk ratio: 1.44, 95% CI: 1.14 to 1.81, p = 0.002; I2 = 46%). Subgroup analyses showed consistent results in studies with LUS score analyzed with 12 or 16 lung regions, and in studies reporting mortality during ICU or within 1-month hospitalization.

CONCLUSION

Our findings suggest that a high LUS score at admission may be associated with a high risk of in-hospital mortality of patients with ARDS.

Toward Smart, Automated Junctional Tourniquets-AI Models to Interpret Vessel Occlusion at Physiological Pressure Points

Hemorrhage is the leading cause of preventable death in both civilian and military medicine. Junctional hemorrhages are especially difficult to manage since traditional tourniquet placement is often not possible. Ultrasound can be used to visualize and guide the caretaker to apply pressure at physiological pressure points to stop hemorrhage. However, this process is technically challenging, requiring the vessel to be properly positioned over rigid boney surfaces and applying sufficient pressure to maintain proper occlusion. As a first step toward automating this life-saving intervention, we demonstrate an artificial intelligence algorithm that classifies a vessel as patent or occluded, which can guide a user to apply the appropriate pressure required to stop flow. Neural network models were trained using images captured from a custom tissue-mimicking phantom and an ex vivo swine model of the inguinal region, as pressure was applied using an ultrasound probe with and without color Doppler overlays. Using these images, we developed an image classification algorithm suitable for the determination of patency or occlusion in an ultrasound image containing color Doppler overlay. Separate AI models for both test platforms were able to accurately detect occlusion status in test-image sets to more than 93% accuracy. In conclusion, this methodology can be utilized for guiding and monitoring proper vessel occlusion, which, when combined with automated actuation and other AI models, can allow for automated junctional tourniquet application.

Ultrasound in the Study of Thoracic Diseases: Innovative Aspects

Thoracic ultrasound (TU) has rapidly gained popularity over the past 10 years. This is in part because ultrasound equipment is available in many settings, more training programmes are educating trainees in this technique, and ultrasound can be done rapidly without exposure to radiation. The aim of this review is to present the most interesting and innovative aspects of the use of TU in the study of thoracic diseases. In pleural diseases, TU has been a real revolution. It helps to differentiate between different types of pleural effusions, guides the performance of pleural biopsies when necessary and is more cost-effective under these conditions, and assists in the decision to remove thoracic drainage after talc pleurodesis. With the advent of COVID19, the use of TU has increased for the study of lung involvement. Nowadays it helps in the diagnosis of pneumonias, tumours and interstitial diseases, and its use is becoming more and more widespread in the Pneumology ward. In recent years, TU guided biopsies have been shown to be highly cost-effective, with other advantages such as the absence of radiation and the possibility of being performed at bedside. The use of contrast in ultrasound to increase the cost-effectiveness of these biopsies is very promising. In the study of the mediastinum and peripheral pulmonary nodules, the introduction of echobronchoscopy has brought about a radical change. It is a fully established technique in the study of lung cancer patients. The introduction of elastography may help to further improve its cost-effectiveness. In critically-ill patients, diaphragmatic ultrasound helps in the assessment of withdrawal of mechanical ventilation, and is now an indispensable tool in the management of these patients. In neuromuscular patients, ultrasound is a good predictor of impaired lung function. Currently, in Neuromuscular Disease Units, TU is an indispensable tool. Ultrasound study of the intercostal musculature is also effective in the study of respiratory function, and is widely used in Respiratory Rehabilitation. In Intermediate Care Units, thoracic ultrasound is indispensable for patient management. In these units there are ultrasound protocols for the management of patients with acute dyspnoea that have proven to be very effective.

The European Respiratory Society led training programme improves self-reported competency and increases the use of thoracic ultrasound

Thoracic ultrasound has become a well-implemented diagnostic tool for assessment and monitoring of patients with respiratory symptoms or disease. However, ultrasound examinations are user dependent and sufficient competencies are needed. The European Respiratory Society (ERS) hosts a structured and evidence-based training programme in thoracic ultrasound. This study aimed to explore and discuss the self-reported activity and self-reported competency of the participants during the ERS course. Online surveys were sent to the training programme participants before the second part of the course (practical part of the course), and before and 3 months after the third part of the course (final certification exam). A total of 77 participants completed the surveys. The self-reported frequency of thoracic ultrasound examinations increased during the course, and in the final survey more than 90% of the participants used thoracic ultrasound on weekly basis. The self-reported competency (on technical execution of the thoracic ultrasound examination and overall competency) also increased. The ERS thoracic ultrasound training programme forms the basis of broad theoretical knowledge and sufficient practical skills that seem to lead to behavioural changes, whereby a large proportion of the participants implemented ultrasound in their clinical practice.

Point-of-Care Lung Ultrasound in the Intensive Care Unit-The Dark Side of Radiology: Where Do We Stand?

Patients in intensive care units (ICUs) are critically ill and require constant monitoring of clinical conditions. Due to the severity of the underlying disease and the need to monitor devices, imaging plays a crucial role in critically ill patients' care. Given the clinical complexity of these patients, who typically need respiratory assistance as well as continuous monitoring of vital functions and equipment, computed tomography (CT) can be regarded as the diagnostic gold standard, although it is not a bedside diagnostic technique. Despite its limitations, portable chest X-ray (CXR) is still today an essential diagnostic tool used in the ICU. Being a widely accessible imaging technique, which can be performed at the patient's bedside and at a low healthcare cost, it provides additional diagnostic support to the patient's clinical management. In recent years, the use of point-of-care lung ultrasound (LUS) in ICUs for procedure guidance, diagnosis, and screening has proliferated, and it is usually performed at the patient's bedside. This review illustrates the role of point-of-care LUS in ICUs from a purely radiological point of view as an advanced method in ICU CXR reports to improve the interpretation and monitoring of lung CXR findings.

COVID-19 in Infants and Children under 2 Years-Could Lung Ultrasound Score Be Correlated with Biomarkers and Symptoms?

INTRODUCTION

It is already well known that infants and children infected with COVID-19 develop mild to moderate forms of the disease, with fever and oropharyngeal congestion being the most common symptoms. However, there are instances when patients claim to be experiencing respiratory symptoms. Because of the repeated lung examinations required in these situations, non-irradiating imaging techniques are preferred. This study's objective is to ascertain the value of lung ultrasonography (LUS) in the medical management of these specific cases.

METHODS

Infants and children under two years old with SARS-CoV-2 infection were evaluated using LUS. Patients with other respiratory pathologies were excluded by using specific tests. The LUS score (LUSS) was correlated with biomarkers and clinical findings using the Mann-Whitney U test and Spearman's rank correlation rho.

RESULTS

The LUSS for each patient varied from 1 to 8 points out of a maximum of 36 points. The arithmetic mean was 4.47 ± 2.36 (S.D), while the 95% CI for the arithmetic mean was 3.33 to 5.61. Sparse B-lines were present in all enrolled infants and children (100%), while only 36.84% developed alveolar syndrome (confluent B-lines). The lung changes were correlated with their biomarkers, specifically inflammatory markers. The correlation between LUSS and LDH, D-dimers, and IL-6 was a strongly positive one with rho = 0.55 (p = 0.001, 95% CI 0.13 to 0.80) between the LUSS and D-dimer levels and rho = 0.60 (p = 0.03, 95% CI 0.04 to 0.87) between LUSS and D-dimer levels at symptomatic infants and children (with respiratory involvement).

CONCLUSIONS

Infants and children under the age of two are prone to develop mild forms of COVID-19 disease with a B-line pattern on LUS, although inflammatory markers have elevated blood levels. Despite the small sample, D-dimer levels and O2 saturation were correlated with LUSS in patients with respiratory involvement, while similar results were also found in the entire lot.

Role of ultrasound in acute respiratory failure and in the weaning of mechanical ventilation

Comprehensive ultrasound assessment has become an essential tool to facilitate the diagnosis and therapeutic management of critically ill patients with acute respiratory failure (ARF). There is evidence supporting the use of ultrasound for the diagnosis of pneumothorax, acute respiratory distress syndrome, cardiogenic pulmonary edema, pneumonia and acute pulmonary thromboembolism, and in patients with COVID-19. In addition, in recent years, the use of ultrasound to evaluate responses to treatment in critically ill patients with ARF has been developed, providing a noninvasive tool for titrating positive end-expiratory pressure, monitoring recruitment maneuvers and response to prone position, as well as for facilitating weaning from mechanical ventilation. The objective of this review is to summarize the basic concepts on the utility of ultrasound in the diagnosis and monitoring of critically ill patients with ARF.

Performance Comparison of Object Detection Networks for Shrapnel Identification in Ultrasound Images

Ultrasound imaging is a critical tool for triaging and diagnosing subjects but only if images can be properly interpreted. Unfortunately, in remote or military medicine situations, the expertise to interpret images can be lacking. Machine-learning image interpretation models that are explainable to the end user and deployable in real time with ultrasound equipment have the potential to solve this problem. We have previously shown how a YOLOv3 (You Only Look Once) object detection algorithm can be used for tracking shrapnel, artery, vein, and nerve fiber bundle features in a tissue phantom. However, real-time implementation of an object detection model requires optimizing model inference time. Here, we compare the performance of five different object detection deep-learning models with varying architectures and trainable parameters to determine which model is most suitable for this shrapnel-tracking ultrasound image application. We used a dataset of more than 16,000 ultrasound images from gelatin tissue phantoms containing artery, vein, nerve fiber, and shrapnel features for training and evaluating each model. Every object detection model surpassed 0.85 mean average precision except for the detection transformer model. Overall, the YOLOv7tiny model had the higher mean average precision and quickest inference time, making it the obvious model choice for this ultrasound imaging application. Other object detection models were overfitting the data as was determined by lower testing performance compared with higher training performance. In summary, the YOLOv7tiny object detection model had the best mean average precision and inference time and was selected as optimal for this application. Next steps will implement this object detection algorithm for real-time applications, an important next step in translating AI models for emergency and military medicine.

Serial lung ultrasound in monitoring viral pneumonia: the lesson learned from COVID-19

Background

Lung ultrasound (LUS) has proven to be useful in the evaluation of lung involvement in COVID-19. However, its effectiveness for predicting the risk of severe disease is still up for debate. The aim of the study was to establish the prognostic accuracy of serial LUS examinations in the prediction of clinical deterioration in hospitalised patients with COVID-19.

Methods

Prospective single-centre cohort study of patients hospitalised for COVID-19. The study protocol consisted of a LUS examination within 24 h from admission and a follow-up examination on day 3 of hospitalisation. Lung involvement was evaluated by a 14-area LUS score. The primary end-point was the ability of LUS to predict clinical deterioration defined as need for intensive respiratory support with high-flow oxygen or invasive mechanical ventilation.

Results

200 patients were included and 35 (17.5%) of them reached the primary end-point and were transferred to the intensive care unit (ICU). The LUS score at admission had been significantly higher in the ICU group than in the non-ICU group (22 (interquartile range (IQR) 20-26) versus 12 (IQR 8-15)). A LUS score at admission ≥17 was shown to be the best cut-off point to discriminate patients at risk of deterioration (area under the curve (AUC) 0.95). The absence of progression in LUS score on day 3 significantly increased the prediction accuracy by ruling out deterioration with a negative predictive value of 99.29%.

Conclusion

Serial LUS is a reliable tool in predicting the risk of respiratory deterioration in patients hospitalised due to COVID-19 pneumonia. LUS could be further implemented in the future for risk stratification of viral pneumonia.

Lung ultrasound score severity cut-off points in COVID-19 pneumonia. A systematic review and validating cohort

OBJECTIVES

Our purpose was to establish different cut-off points based on the lung ultrasound score (LUS) to classify COVID-19 pneumonia severity.

METHODS

Initially, we conducted a systematic review among previously proposed LUS cut-off points. Then, these results were validated by a single-centre prospective cohort study of adult patients with confirmed SARS-CoV-2 infection. Studied variables were poor outcome (ventilation support, intensive care unit admission or 28-days mortality) and 28-days mortality.

RESULTS

From 510 articles, 11 articles were included. Among the cut-off points proposed in the articles included, only the LUS>15 cut-off point could be validated for its original endpoint, demonstrating also the strongest relation with poor outcome (odds ratio [OR]=3.636, confidence interval [CI] 1.411-9.374). Regarding our cohort, 127 patients were admitted. In these patients, LUS was statistically associated with poor outcome (OR=1.303, CI 1.137-1.493), and with 28-days mortality (OR=1.024, CI 1.006-1.042). LUS>15 showed the best diagnostic performance when choosing a single cut-off point in our cohort (area under the curve 0.650). LUS≤7 showed high sensitivity to rule out poor outcome (0.89, CI 0.695-0.955), while LUS>20 revealed high specificity to predict poor outcome (0.86, CI 0.776-0.917).

CONCLUSIONS

LUS is a good predictor of poor outcome and 28-days mortality in COVID-19. LUS≤7 cut-off point is associated with mild pneumonia, LUS 8-20 with moderate pneumonia and ≥20 with severe pneumonia. If a single cut-off point were used, LUS>15 would be the point which better discriminates mild from severe disease.

Lung ultrasound score severity cut-off points in COVID-19 pneumonia. A systematic review and validating cohort

Objectives

Our purpose was to establish different cut-off points based on the lung ultrasound score (LUS) to classify COVID-19 pneumonia severity.

Methods

Initially, we conducted a systematic review among previously proposed LUS cut-off points. Then, these results were validated by a single-centre prospective cohort study of adult patients with confirmed SARS-CoV-2 infection. Studied variables were poor outcome (ventilation support, intensive care unit admission or 28-days mortality) and 28-days mortality.

Results

From 510 articles, 11 articles were included. Among the cut-off points proposed in the articles included, only the LUS > 15 cut-off point could be validated for its original endpoint, demonstrating also the strongest relation with poor outcome (odds ratio [OR] = 3.636, confidence interval [CI] 1.411-9.374). Regarding our cohort, 127 patients were admitted. In these patients, LUS was statistically associated with poor outcome (OR = 1.303, CI 1.137-1.493), and with 28-days mortality (OR = 1.024, CI 1.006-1.042). LUS > 15 showed the best diagnostic performance when choosing a single cut-off point in our cohort (area under the curve 0.650). LUS ≤ 7 showed high sensitivity to rule out poor outcome (0.89, CI 0.695-0.955), while LUS > 20 revealed high specificity to predict poor outcome (0.86, CI 0.776-0.917).

Conclusions

LUS is a good predictor of poor outcome and 28-days mortality in COVID-19. LUS ≤ 7 cut-off point is associated with mild pneumonia, LUS 8-20 with moderate pneumonia and ≥20 with severe pneumonia. If a single cut-off point were used, LUS > 15 would be the point which better discriminates mild from severe disease.

Evidence-based training and certification: the ERS thoracic ultrasound training programme

Thoracic ultrasound has developed into an integral part of the respiratory physician's diagnostic and therapeutic toolbox, with high diagnostic accuracy for many diseases causing acute or chronic respiratory symptoms. However, it is vitally important that the operator has received the appropriate education and training to ensure a systematic and thorough examination, correct image interpretation, and that they then have the appropriate skills to integrate all the findings for patient benefit. In this review, we present the new European Respiratory Society thoracic ultrasound training programme, including a discussion of curriculum development, its implementation, and trainee evaluation. This programme enables participants to gain competence in thoracic ultrasound through structured, evidence-based training with robustly validated assessments and certification. The training programme consists of three components: an online, theoretical part (part 1), which is accessible all year; a practical course (part 2), with four courses held each year (two online courses and two on-site courses); and an examination (part 3) comprising an objective structured clinical examination (OSCE), which is hosted each year at the European Respiratory Society Congress.

Role of Imaging in the Management of Patients with SARS-CoV-2 Lung Involvement Admitted to the Emergency Department: A Systematic Review

During the waves of the coronavirus disease (COVID-19) pandemic, emergency departments were overflowing with patients suffering with suspected medical or surgical issues. In these settings, healthcare staff should be able to deal with different medical and surgical scenarios while protecting themselves against the risk of contamination. Various strategies were used to overcome the most critical issues and guarantee quick and efficient diagnostic and therapeutic charts. The use of saliva and nasopharyngeal swab Nucleic Acid Amplification Tests (NAAT) in the diagnosis of COVID-19 was one of the most adopted worldwide. However, NAAT results were slow to report and could sometimes create significant delays in patient management, especially during pandemic peaks. On these bases, radiology has played and continues to play an essential role in detecting COVID-19 patients and solving differential diagnosis between different medical conditions. This systematic review aims to summarize the role of radiology in the management of COVID-19 patients admitted to emergency departments by using chest X-rays (CXR), computed tomography (CT), lung ultrasounds (LUS), and artificial intelligence (AI).

Lung Ultrasound in Critical Care and Emergency Medicine: Clinical Review

Lung ultrasound has become a part of the daily examination of physicians working in intensive, sub-intensive, and general medical wards. The easy access to hand-held ultrasound machines in wards where they were not available in the past facilitated the widespread use of ultrasound, both for clinical examination and as a guide to procedures; among point-of-care ultrasound techniques, the lung ultrasound saw the greatest spread in the last decade. The COVID-19 pandemic has given a boost to the use of ultrasound since it allows to obtain a wide range of clinical information with a bedside, not harmful, repeatable examination that is reliable. This led to the remarkable growth of publications on lung ultrasounds. The first part of this narrative review aims to discuss basic aspects of lung ultrasounds, from the machine setting, probe choice, and standard examination to signs and semiotics for qualitative and quantitative lung ultrasound interpretation. The second part focuses on how to use lung ultrasound to answer specific clinical questions in critical care units and in emergency departments.

Repeated Lung Ultrasound versus Chest X-ray-Which One Predicts Better Clinical Outcome in COVID-19?

The purpose of this study was to evaluate whether changes in repeated lung ultrasound (LUS) or chest X-ray (CXR) of coronavirus disease 2019 (COVID-19) patients can predict the development of severe disease and the need for treatment in the intensive care unit (ICU). In this prospective monocentric study, COVID-19 patients received standardized LUS and CXR at day 1, 3 and 5. Scores for changes in LUS (LUS score) and CXR (RALE and M-RALE) were calculated and compared. Intra-class correlation was calculated for two readers of CXR and ROC analysis to evaluate the best discriminator for the need for ICU treatment. A total of 30 patients were analyzed, 26 patients with follow-up LUS and CXR. Increase in M-RALE between baseline and follow-up 1 was significantly higher in patients with need for ICU treatment in the further hospital stay (p = 0.008). Both RALE and M-RALE significantly correlated with LUS score (r = 0.5, p < 0.0001). ROC curves with need for ICU treatment as separator were not significantly different for changes in M-RALE (AUC: 0.87) and LUS score (AUC: 0.79), both being good discriminators. ICC was moderate for RALE (0.56) and substantial for M-RALE (0.74). The present study demonstrates that both follow-up LUS and CXR are powerful tools to track the evolution of COVID-19, and can be used equally as predictors for the need for ICU treatment.

A survey of barriers and facilitators to ultrasound use in low- and middle-income countries

Point-of-care ultrasound has the potential to help inform assessment, diagnosis, and management of illness in low- and middle-income countries (LMIC). To better understand current ultrasound use, barriers and facilitators to use, and perceptions and practices in LMIC, we conducted an anonymous online global survey targeting healthcare providers training and using ultrasound in LMIC. A total of 241 respondents representing 62 countries participated and most were physicians working in publicly-funded urban tertiary hospitals in LMIC. Most had received ultrasound training (78%), reported expertise (65%) and confidence (90%) in ultrasound use, and had access to ultrasound (88%), utilizing ultrasound most commonly for procedures and for evaluations of lungs, heart, and trauma. Access to an ultrasound machine was reported as both the top barrier (17%) and top facilitator (53%); other common barriers included access to education and training, cost, and competition for use and other common facilitators included access to a probe, gel, and electricity, and acceptance by healthcare providers, administrators, and patients. Most (80%) noted ultrasound access was important and 96% agreed that ultrasound improves quality of care and patient outcomes. Improving access to low-cost ultrasound equipment is critical to increasing ultrasound use among those who are trained.

The use of lung ultrasound in COVID-19

This review article addresses the role of lung ultrasound in patients with coronavirus disease 2019 (COVID-19) for diagnosis and disease management. As a simple imaging procedure, lung ultrasound contributes to the early identification of patients with clinical conditions suggestive of COVID-19, supports decisions about hospital admission and informs therapeutic strategy. It can be performed in various clinical settings (primary care facilities, emergency departments, hospital wards, intensive care units), but also in outpatient settings using portable devices. The article describes typical lung ultrasound findings for COVID-19 pneumonia (interstitial pattern, pleural abnormalities and consolidations), as one component of COVID-19 diagnostic workup that otherwise includes clinical and laboratory evaluation. Advantages and limitations of lung ultrasound use in COVID-19 are described, along with equipment requirements and training needs. To infer on the use of lung ultrasound in different regions, a literature search was performed using key words "COVID-19", "lung ultrasound" and "imaging". Lung ultrasound is a noninvasive, rapid and reproducible procedure; can be performed at the point of care; requires simple sterilisation; and involves non-ionising radiation, allowing repeated exams on the same patient, with special benefit in children and pregnant women. However, physical proximity between the patient and the ultrasound operator is a limitation in the current pandemic context, emphasising the need to implement specific infection prevention and control measures. Availability of qualified staff adequately trained to perform lung ultrasound remains a major barrier to lung ultrasound utilisation. Training, advocacy and awareness rising can help build up capacities of local providers to facilitate lung ultrasound use for COVID-19 management, in particular in low- and middle-income countries.

The Global Impact of COVID-19: Historical Development, Molecular Characterization, Drug Discovery and Future Directions

In December 2019, an outbreak of a respiratory disease called the coronavirus disease 2019 (COVID-19) caused by a new coronavirus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began in Wuhan, China. The SARS-CoV-2, an encapsulated positive-stranded RNA virus, spread worldwide with disastrous consequences for people's health, economies, and quality of life. The disease has had far-reaching impacts on society, including economic disruption, school closures, and increased stress and anxiety. It has also highlighted disparities in healthcare access and outcomes, with marginalized communities disproportionately affected by the SARS-CoV-2. The symptoms of COVID-19 range from mild to severe. There is presently no effective cure. Nevertheless, significant progress has been made in developing COVID-19 vaccine for different therapeutic targets. For instance, scientists developed multifold vaccine candidates shortly after the COVID-19 outbreak after Pfizer and AstraZeneca discovered the initial COVID-19 vaccines. These vaccines reduce disease spread, severity, and mortality. The addition of rapid diagnostics to microscopy for COVID-19 diagnosis has proven crucial. Our review provides a thorough overview of the historical development of COVID-19 and molecular and biochemical characterization of the SARS-CoV-2. We highlight the potential contributions from insect and plant sources as anti-SARS-CoV-2 and present directions for future research.

Reliability and clinical correlations of semi-quantitative lung ultrasound on BLUE points in COVID-19 mechanically ventilated patients: The 'BLUE-LUSS'-A feasibility clinical study

INTRODUCTION

Bedside lung ultrasound has gained a key role in each segment of the treatment chain during the COVID-19 pandemic. During the diagnostic assessment of the critically ill patients in ICUs, it is highly important to maximize the amount and quality of gathered information while minimizing unnecessary interventions (e.g. moving/rotating the patient). Another major factor is to reduce the risk of infection and the workload of the staff.

OBJECTIVES

To serve these significant issues we constructed a feasibility study, in which we used a single-operator technique without moving the patient, only assessing the easily achievable lung regions at conventional BLUE points. We hypothesized that calculating this 'BLUE lung ultrasound score' (BLUE-LUSS) is a reasonable clinical tool. Furthermore, we used both longitudinal and transverse scans to measure their reliability and assessed the interobserver variability as well.

METHODS

University Intensive Care Unit based, single-center, prospective, observational study was performed on 24 consecutive SARS-CoV2 RT-PCR positive, mechanically ventilated critically ill patients. Altogether 400 loops were recorded, rated and assessed off-line by 4 independent intensive care specialists (each 7+ years of LUS experience).

RESULTS

Intraclass correlation values indicated good reliability for transversal and longitudinal qLUSS scores, while we detected excellent interrater agreement of both cLUSS calculation methods. All of our LUS scores correlated inversely and significantly to the P/F values. Best correlation was achieved in the case of longitudinal qLUSS (r = -0.55, p = 0.0119).

CONCLUSION

Summarized score of BLUE-LUSS can be an important, easy-to-perform adjunct tool for assessing and quantifying lung pathology in critically ill ventilated patients at bedside, especially for the P/F ratio. The best agreement for the P/F ratio can be achieved with the longitudinal scans. Regarding these findings, assessing BLUE-points can be extended with the BLUE-LUSS for daily routine using both transverse and longitudinal views.

Use of POCUS in Chest Pain and Dyspnea in Emergency Department: What Role Could It Have?

Chest pain and dyspnea are common symptoms in patients presenting to the emergency room (ER); oftentimes it is not possible to clearly identify the underlying cause, which may cause the patient to have to return to the ER. In other cases, while it is possible to identify the underlying cause, it is necessary to perform a large number of tests before being able to make a diagnosis. Over the last twenty years, emergency medicine physicians have had the possibility of using ultrasound to help them make and rule out diagnoses. Specific ultrasound tests have been designed to evaluate patients presenting with specific symptoms to ensure a fast, yet complete, evaluation. In this paper, we examine the role of ultrasound in helping physicians understand the etiology behind chest pain and dyspnea. We analyze the different diseases and disorders which may cause chest pain and dyspnea as symptoms and discuss the corresponding ultrasound findings.

COVID-19 related maternal mortality cases in associated with Delta and Omicron waves and the role of lung ultrasound

Objective

To present coronavirus disease-2019 (COVID-19) related maternal mortality in relation to Delta and Omicron waves and to investigate the role of lung ultrasound (LUS) in estimating mortality.

Materials and Methods

This retrospective cohort study was conducted in the obstetrics and gynecology clinic of a tertiary pandemic hospital between March 2020 and January 2022. The hospitalized pregnant women with COVID-19 diagnosis and maternal deaths were studied in relation with Delta and Omicron waves. The relationship between LUS scores of hospitalized patients and maternal mortality was explored.

Results

Thousand and sixty-five pregnant women were hospitalized because of COVID-19 infection. Fifty-one (4.79%) of these patients had critical sickness, 96 (9.01%) of them had severe illness, 62 (5.82%) of them were admitted to the intensive care unit and 28 (2.63%) of all hospitalized pregnant women had died. Of the 1.065 patients, 783 (73.5%) were hospitalized before the Delta wave and the maternal mortality rate was 1.28% (10/783), 243 (22.8%) were hospitalized during the Delta wave and the maternal mortality rate was 7% (17/243) [relative risk (RR)=5.478, 95% confidence interval (CI) (2.54-11.8), z=4.342, p<0.001]. During the Omicron wave 39 (3.66%) patients were hospitalized and the maternal mortality rate was 2.56% (1/39). Maternal mortality rates, according to LUS scores, were 0.37% (1/273) for LUS 0, 0.72% (2/277) for LUS 1, 2.58% (10/387) for LUS 2 and 11.72% (15/128) for LUS 3 respectively (LUS 3 vs. others; maternal mortality: RR=8.447, 95% CI (4.11-17.34), z=5.814, p<0.0001). There were no vaccinated patients in the study cohort.

Conclusion

The maternal mortality rate was relatively high, particularly during the Delta wave at our referral center. The Delta wave, delayed vaccination and vaccine hesitancy of pregnant women might have important roles in maternal mortality. Higher LUS scores should warn clinicians of an increased risk of maternal death.

Feasibility, Prediction and Association of Right Ventricular Free Wall Longitudinal Strain with 30-Day Mortality in Severe COVID-19 Pneumonia: A Prospective Study

Introduction: Right ventricular (RV) systolic dysfunction (RVsD) is a common complication of coronavirus infection 2019 disease (COVID-19). The right ventricular free wall longitudinal strain parameter (RV-FWLS) is a powerful predictor of mortality. We explored the performance of RVsD parameters for predicting 30-day mortality and the association between RV-FWLS and 30-day mortality. Methods: COVID-19 patients hospitalized at Amiens University Hospital in the critical care unit with transthoracic echocardiography were included. We measured tricuspid annular plane systolic excursion (TAPSE), the RV S’ wave, RV fractional area change (RV-FAC), and RV-FWLS. The diagnostic performance of RVsD parameters as predictors for 30-day mortality was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). RVsD was defined by an RV-FWLS < 21% to explore the association between RVsD and 30-day mortality. Results: Of the 116 patients included, 20% (n = 23/116) died and 47 had a RVsD. ROC curve analysis showed that RV-FWLS failed to predict 30-day mortality, as did conventional RV parameters (all p > 0.05). TAPSE (21 (19−26) mm vs. 24 (21−27) mm; p = 0.024) and RV-FAC (40 (35−47)% vs. 47 (41−55)%; p = 0.006) were lowered in the RVsD group. In Cox analysis, RVsD was not associated with 30-day mortality (hazard ratio = 1.12, CI 95% (0.49−2.55), p = 0.78). Conclusion: In severe COVID-19 pneumonia, RV-FWLS was not associated with 30-day mortality.

Applications of artificial intelligence in lung ultrasound: Review of deep learning methods for COVID-19 fighting

BACKGROUND

The pandemic outbreak of the novel coronavirus disease (COVID-19) has highlighted the need to combine rapid, non-invasive and widely accessible techniques with the least risk of patient’s cross-infection to achieve a successful early detection and surveillance of the disease. In this regard, the lung ultrasound (LUS) technique has been proved invaluable in both the differential diagnosis and the follow-up of COVID-19 patients, and its potential may be destined to evolve. Recently, indeed, LUS has been empowered through the development of automated image processing techniques.

AIM

To provide a systematic review of the application of artificial intelligence (AI) technology in medical LUS analysis of COVID-19 patients using the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines.

METHODS

A literature search was performed for relevant studies published from March 2020 - outbreak of the pandemic - to 30 September 2021. Seventeen articles were included in the result synthesis of this paper.

RESULTS

As part of the review, we presented the main characteristics related to AI techniques, in particular deep learning (DL), adopted in the selected articles. A survey was carried out on the type of architectures used, availability of the source code, network weights and open access datasets, use of data augmentation, use of the transfer learning strategy, type of input data and training/test datasets, and explainability.

CONCLUSION

Finally, this review highlighted the existing challenges, including the lack of large datasets of reliable COVID-19-based LUS images to test the effectiveness of DL methods and the ethical/regulatory issues associated with the adoption of automated systems in real clinical scenarios.

Can Lung Ultrasound Be the Ideal Monitoring Tool to Predict the Clinical Outcome of Mechanically Ventilated COVID-19 Patients? An Observational Study

Background: During the COVID-19 pandemic, lung ultrasound (LUS) has been widely used since it can be performed at the patient’s bedside, does not produce ionizing radiation, and is sufficiently accurate. The LUS score allows for quantifying lung involvement; however, its clinical prognostic role is still controversial. Methods: A retrospective observational study on 103 COVID-19 patients with respiratory failure that were assessed with an LUS score at intensive care unit (ICU) admission and discharge in a tertiary university COVID-19 referral center. Results: The deceased patients had a higher LUS score at admission than the survivors (25.7 vs. 23.5; p-value = 0.02; cut-off value of 25; Odds Ratio (OR) 1.1; Interquartile Range (IQR) 1.0−1.2). The predictive regression model shows that the value of LUSt0 (OR 1.1; IQR 1.0–1.3), age (OR 1.1; IQR 1.0−1.2), sex (OR 0.7; IQR 0.2−3.6), and days in spontaneous breathing (OR 0.2; IQR 0.1–0.5) predict the risk of death for COVID-19 patients (Area under the Curve (AUC) 0.92). Furthermore, the surviving patients showed a significantly lower difference between LUS scores at admission and discharge (mean difference of 1.75, p-value = 0.03). Conclusion: Upon entry into the ICU, the LUS score may play a prognostic role in COVID-19 patients with ARDS. Furthermore, employing the LUS score as a monitoring tool allows for evaluating the patients with a higher probability of survival.