Lung ultrasonography (LUS) is a point-of-care imaging modality with growing potential in primary care.

While its use is well established in hospital settings, data on its accuracy when performed by general practitioners (GPs) remain limited. This study aimed to assess the diagnostic accuracy of LUS conducted by GPs following structured training.

We recruited 17 GPs from various regions of the Czech Republic. They completed a two-day educational course focused on LUS. Patients with current dyspnoea (NYHA II-IV) or a history of dyspnoea within the last four weeks were included and underwent LUS to assess the presence of pleural effusion and interstitial syndrome. An independent expert sonographer, blinded to clinical data, evaluated recorded LUS video loops as the reference standard. LUS findings were categorized into A profile (presence of A lines and intact lung sliding, indicating normal aeration), B profile (three or more B lines per intercostal space in at least two intercostal spaces per hemithorax, suggesting interstitial syndrome), pulmonary consolidation and pleural effusion.

A total of 128 patients were enrolled in the study. A total of 768 thoracic segments were examined. A profile was identified in 642 (83.6%) segments, B profile in 108 (14.1%), pulmonary consolidation in 8 (1.0%), and pleural effusion in 12 (1.6%). For the identification of A profile, the sensitivity was 97.51% (95% CI 95.98-98.57), and the specificity was 88.10% (95% CI 81,13-93,18); for B profile, the sensitivity was 87.04% (95% CI 79,21-92,73), and the specificity was 97.73% (95% CI96,28-98,72); for pulmonary consolidation, the sensitivity was 100.0% (95% CI 63,06-100,00), and the specificity was 100.0% (95% CI 99,52-100,0); for pleural effusion, the sensitivity was 83.33% (95% CI 51,59-97,91), and the specificity was 99.87% (95% CI 99,27-100,00).

Our findings provide important preliminary data, demonstrating that GPs can perform LUS accurately after a structured training program. THE TRIAL REGISTRATION IDENTIFIER: is NCT04905719.

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.

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.

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.

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.

Transaxial CT imaging is the main clinical imaging modality for the assessment of COVID-induced lung damage. However, this type of data does not quantify the functional properties of the lung. The objective is to provide non-invasive personalized cartographies of lung stiffness for long-COVID patients using MR elastography (MRE) and follow-up the evolution of this quantitative mapping over time.

Seven healthy and seven long-COVID participants underwent CT and MRE imaging at total lung capacity. After CT test, a senior radiologist visually analyzed the lung structure. Less than one month later, a first MRI (1.5 T, GRE sequence) lung density test followed by a first MRE (SE-EPI sequence) test were performed. Gadolinium-doped water phantom and a pneumatic driver (vibration frequency: 50 Hz), placed on the sternum, were used for MRI and MRE tests, respectively. Personalized cartographies of the stiffness were obtained, by two medical imaging engineers, using a specific post processing (MMDI algorithm). The monitoring (lung density, stiffness) was carried out no later than 11 months for each COVID patient. Wilcoxon's tests and an intra-class correlation coefficient (ICC) were used for statistical analysis.

The density for long-COVID patients was significantly (P = 0.047) greater (170 kg.m-3) compared to healthy (125 kg.m-3) subjects. After the first MRE test, the stiffness measured for the healthy subjects was in the same range (median value (interquartile range, IQR): 0.93 (0.09) kPa), while the long-COVID patients showed a larger stiffness range (from 1.39 kPa to 2.05 kPa). After a minimum delay of 5 months, the second MRE test showed a decrease of stiffness (from 22 % to 40 %) for every long-COVID patient. The inter-operator agreement was excellent (intra-class correlation coefficient: 0.93 [0.78-0.97]).

The MRE test is sensitive enough to monitor disease-induced change in lung stiffness (increase with COVID symptoms and decrease with recovery). This non-invasive modality could yield complementary information as a new imaging biomarker to follow up long-COVID patients.

Point of Care ultrasound (POCUS) of the lungs, also known as lung ultrasound (LUS), has emerged as a technique that allows for the diagnosis of many respiratory pathologies with greater accuracy and speed compared to conventional techniques such as chest x-ray and auscultation. The goal of this narrative review is to provide a simple and practical approach to LUS for critical care, pulmonary, and anesthesia providers, as well as respiratory therapists and other health care providers to be able to implement this technique into their clinical practice. In this review, we will discuss the basic physics of LUS, provide a hands-on scanning technique, describe LUS findings seen in normal and pathological conditions (such as mainstem intubation, pneumothorax, atelectasis, pneumonia, aspiration, COPD exacerbation, cardiogenic pulmonary edema, ARDS, and pleural effusion) and also review the training necessary to achieve competence in LUS.

COVID-19 pneumonia severity assessment is of great clinical importance, and lung ultrasound (LUS) plays a crucial role in aiding the severity assessment of COVID-19 pneumonia due to its safety and portability. However, its reliance on qualitative and subjective observations by clinicians is a limitation. Moreover, LUS images often exhibit significant heterogeneity, emphasizing the need for more quantitative assessment methods. In this paper, we propose a knowledge fused latent representation framework tailored for COVID-19 pneumonia severity assessment using LUS examinations. The framework transforms the LUS examination into latent representation and extracts knowledge from regions labeled by clinicians to improve accuracy. To fuse the knowledge into the latent representation, we employ a knowledge fusion with latent representation (KFLR) model. This model significantly reduces errors compared to approaches that lack prior knowledge integration. Experimental results demonstrate the effectiveness of our method, achieving high accuracy of 96.4 % and 87.4 % for binary-level and four-level COVID-19 pneumonia severity assessments, respectively. It is worth noting that only a limited number of studies have reported accuracy for clinically valuable exam level assessments, and our method surpass existing methods in this context. These findings highlight the potential of the proposed framework for monitoring disease progression and patient stratification in COVID-19 pneumonia cases.

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.

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.

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.

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.

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.

To synthesize the current evidence of maternal point-of-care ultrasound (POCUS) in obstetrics. A scoping review was conducted using PubMed, Clinicaltrials.gov, and the Cochrane library from inception through October 2023.

Studies were eligible for inclusion if they described the use of POCUS among obstetric or postpartum patients. Two authors independently screened all abstracts. Quantitative, qualitative, and mixed-methods studies were eligible for inclusion. Case reports of single cases, review articles, and expert opinion articles were excluded. Studies describing detailed maternal nonobstetric sonograms or maternal first trimester sonograms to confirm viability and rule out ectopic pregnancy were also excluded. Data were tabulated using Microsoft Excel and summarized using a narrative review and descriptive statistics.

A total of 689 publications were identified through the search strategy and 12 studies met the inclusion criteria. Nine studies evaluated the use of lung POCUS in obstetrics in different clinical scenarios. Lung ultrasound (LUS) findings in preeclampsia showed an excellent ability to detect pulmonary edema (area under the receiver operating characteristic 0.961) and findings were correlated with clinical evidence of respiratory distress (21 of 57 [37%] versus 14 of 109 [13%]; P = .001). Three studies evaluated abdominal POCUS, two of the inferior vena cava (IVC) to predict postspinal anesthesia hypotension (PSAH) and fluid receptivity and one to assess the rate of ascites in patients with preeclampsia. Patients with PSAH had higher IVC collapsibility (area under the curve = 0.950, P < .001) and, in patients with severe preeclampsia, there is a high rate of ascites (52%) associated with increased risk of adverse outcomes. There were no studies on the use of subjective cardiac POCUS.

POCUS use in the management of high-risk obstetrics has increased. LUS has been the most studied modality and appears to have a potential role in the setting of preeclampsia complicated by pulmonary edema. Cardiac and abdominal POCUS have not been well studied. Trials are needed to evaluate its clinical applicability, reliability, and technique standardization before widespread use.

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.

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.

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).

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.

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.

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.

Many virological tests have been implemented during the Coronavirus Disease 2019 (COVID-19) pandemic for diagnostic purposes, but they appear unsuitable for screening purposes. Furthermore, current screening strategies are not accurate enough to effectively curb the spread of the disease. Therefore, the present study was conducted within a controlled clinical environment to determine eventual detectable variations in the voice of COVID-19 patients, recovered and healthy subjects, and also to determine whether machine learning-based voice assessment (MLVA) can accurately discriminate between them, thus potentially serving as a more effective mass-screening tool. Three different subpopulations were consecutively recruited: positive COVID-19 patients, recovered COVID-19 patients and healthy individuals as controls. Positive patients were recruited within 10 days from nasal swab positivity. Recovery from COVID-19 was established clinically, virologically and radiologically. Healthy individuals reported no COVID-19 symptoms and yielded negative results at serological testing. All study participants provided three trials for multiple vocal tasks (sustained vowel phonation, speech, cough). All recordings were initially divided into three different binary classifications with a feature selection, ranking and cross-validated RBF-SVM pipeline. This brough a mean accuracy of 90.24%, a mean sensitivity of 91.15%, a mean specificity of 89.13% and a mean AUC of 0.94 across all tasks and all comparisons, and outlined the sustained vowel as the most effective vocal task for COVID discrimination. Moreover, a three-way classification was carried out on an external test set comprised of 30 subjects, 10 per class, with a mean accuracy of 80% and an accuracy of 100% for the detection of positive subjects. Within this assessment, recovered individuals proved to be the most difficult class to identify, and all the misclassified subjects were declared positive; this might be related to mid and short-term vocal traces of COVID-19, even after the clinical resolution of the infection. In conclusion, MLVA may accurately discriminate between positive COVID-19 patients, recovered COVID-19 patients and healthy individuals. Further studies should test MLVA among larger populations and asymptomatic positive COVID-19 patients to validate this novel screening technology and test its potential application as a potentially more effective surveillance strategy for COVID-19.

Introduction Computed tomography (CT) has a high sensitivity for diagnosing COVID-19 pneumonia in critically ill patients, but it has significant limitations. Lung ultrasonography (LUS) is an imaging method increasingly used in intensive care units. Our primary aim is to evaluate the relationship between LUS and CT images by scoring a critically ill patient who was previously diagnosed with COVID-19 pneumonia and underwent CT, as well as to determine their relationship with the patient's oxygenation. Methods This was a single-center, prospective observational study. The study included COVID-19 patients (positive reverse transcription polymerase chain reaction, RT-PCR) who were admitted to the intensive care unit between June 2020 and December 2020, whose oxygen saturation (SpO2) was below 92%, and who underwent a chest tomography scan within the last 12 hours. CT findings were scored by the radiologist using the COVID-19 Reporting and Data System (CO-RADS). The intensivist evaluated 12 regions to determine the LUS score. The ratio of the partial pressure of oxygen in the arterial blood to the inspiratory oxygen concentration (PaO2/FiO2) was used to assess the patient's oxygenation. Results The study included 30 patients and found a weak correlation (ICC = 0.45, 95% CI = 0.25-0.65, p < 0.05) between total scores obtained from LUS and CT scans. The correlation between the total LUS score and oxygenation (r = -0.514, p = 0.004) was stronger than that between the CT score and oxygenation (r = -0.400, p = 0.028). The most common sonographic findings were abnormalities in the pleural line, white lung, and subpleural consolidation. On the other hand, the CT images revealed dense ground-glass opacities and consolidation patterns classified as CO-RADS 5. Conclusion A weak correlation was found between LUS and CT scores in critically ill COVID-19 pneumonia patients. Also, as both scores increased, oxygenation was detected to be impaired, and such a correlation is more evident with the LUS score.

In the realm of emergency medicine, the swift adoption of lung ultrasound (LU) has extended from the adult population to encompass pediatric and neonatal intensivists. LU stands out as a bedside, replicable, and cost-effective modality, distinct in its avoidance of ionizing radiations, a departure from conventional chest radiography. Recent years have witnessed a seamless adaptation of experiences gained in the adult setting to the neonatal and pediatric contexts, underscoring the versatility of bedside Point of care ultrasound (POCUS). This adaptability has proven reliable in diagnosing common pathologies and executing therapeutic interventions, including chest drainage, and central and peripheral vascular cannulation. The surge in POCUS utilization among neonatologists and pediatric intensivists is notable, spanning economically advanced Western nations with sophisticated, high-cost intensive care facilities and extending to low-income countries. Within the neonatal and pediatric population, POCUS has become integral for diagnosing and monitoring respiratory infections and chronic and acute lung pathologies. This, in turn, contributes to a reduction in radiation exposure during critical periods of growth, thereby mitigating oncological risks. Collaboration among various national and international societies has led to the formulation of guidelines addressing both the clinical application and regulatory aspects of operator training. Nevertheless, unified guidelines specific to the pediatric and neonatal population remain lacking, in contrast to the well-established protocols for adults. The initial application of POCUS in neonatal and pediatric settings centered on goal-directed echocardiography. Pivotal developments include expert statements in 2011, the UK consensus statement on echocardiography by neonatologists, and European training recommendations. The Australian Clinician Performed Ultrasound (CPU) program has played a crucial role, providing a robust academic curriculum tailored for training neonatologists in cerebral and cardiac assessment. Notably, the European Society for Paediatric and Neonatal Intensive Care (ESPNIC) recently disseminated evidence-based guidelines through an international panel, delineating the use and applications of POCUS in the pediatric setting. These guidelines are pertinent to any professional tending to critically ill children in routine or emergency scenarios. In light of the burgeoning literature, this paper will succinctly elucidate the methodology of performing an LU scan and underscore its primary indications in the neonatal and pediatric patient cohort. The focal points of this review comprise as follows: (1) methodology for conducting a lung ultrasound scan, (2) key ultrasonographic features characterizing a healthy lung, and (3) the functional approach: Lung Ultrasound Score in the child and the neonate.  Conclusion: the aim of this review is to discuss the following key points: 1. How to perform a lung ultrasound scan 2. Main ultrasonographic features of the healthy lung 3. The functional approach: Lung Ultrasound Score in the child and the neonate What is Known: • Lung Ultrasound (LUS) is applied in pediatric and neonatal age for the diagnosis of pneumothorax, consolidation, and pleural effusion. • Recently, LUS has been introduced into clinical practice as a bedside diagnostic method for monitoring surfactant use in NARDS and lung recruitment in PARDS. What is New: • Lung Ultrasound (LUS) has proven to be useful in confirming diagnoses of pneumothorax, consolidation, and pleural effusion. • Furthermore, it has demonstrated effectiveness in monitoring the response to surfactant therapy in neonates, in staging the severity of bronchiolitis, and in PARDS.

The number and distribution of lung ultrasound (LUS) imaging artifacts termed B-lines correlate with the presence of acute lung disease such as infection, acute respiratory distress syndrome (ARDS), and pulmonary edema. Detection and interpretation of B-lines require dedicated training and is machine and operator-dependent. The goal of this study was to identify radio frequency (RF) signal features associated with B-lines in a cohort of patients with cardiogenic pulmonary edema. A quantitative signal indicator could then be used in a single-element, non-imaging, wearable, automated lung ultrasound sensor (LUSS) for continuous hands-free monitoring of lung fluid.

In this prospective study a 10-zone LUS exam was performed in 16 participants, including 12 patients admitted with acute cardiogenic pulmonary edema (mean age 60 ± 12 years) and 4 healthy controls (mean age 44 ± 21). Overall,160 individual LUS video clips were recorded. The LUS exams were performed with a phased array probe driven by an open-platform ultrasound system with simultaneous RF signal collection. RF data were analyzed offline for candidate B-line indicators based on signal amplitude, temporal variability, and frequency spectrum; blinded independent review of LUS images for the presence or absence of B-lines served as ground truth. Predictive performance of the signal indicators was determined with receiving operator characteristic (ROC) analysis with k-fold cross-validation.

Two RF signal features-temporal variability of signal amplitude at large depths and at the pleural line-were strongly associated with B-line presence. The sensitivity and specificity of a combinatorial indicator were 93.2 and 58.5%, respectively, with cross-validated area under the ROC curve (AUC) of 0.91 (95% CI = 0.80-0.94).

A combinatorial signal indicator for use with single-element non-imaging LUSS was developed to facilitate continuous monitoring of lung fluid in patients with respiratory illness.

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.

Owing to the miniaturization of diagnostic ultrasound scanners and their spread of their bedside use, ultrasonography has been actively utilized in emergency situations. Ultrasonography performed by medical personnel with focused approaches at the bedside for clinical decision-making and improving the quality of invasive procedures is now called point-of-care ultrasonography (POCUS). The concept of POCUS has spread worldwide; however, in Japan, formal clinical guidance concerning POCUS is lacking, except for the application of focused assessment with sonography for trauma (FAST) and ultrasound-guided central venous cannulation. The Committee for the Promotion of POCUS in the Japanese Association for Acute Medicine (JAAM) has often discussed improving the quality of acute care using POCUS, and the "Clinical Guidance for Emergency and Point-of-Care Ultrasonography" was finally established with the endorsement of JAAM. The background, targets for acute care physicians, rationale based on published articles, and integrated application were mentioned in this guidance. The core points include the fundamental principles of ultrasound, airway, chest, cardiac, abdominal, and deep venous ultrasound, ultrasound-guided procedures, and the usage of ultrasound based on symptoms. Additional points, which are currently being considered as potential core points in the future, have also been widely mentioned. This guidance describes the overview and future direction of ultrasonography for acute care physicians and can be utilized for emergency ultrasound education. We hope this guidance will contribute to the effective use of ultrasonography in acute care settings in Japan.

Imaging has been demonstrated to play a crucial role in both the diagnosis and management of COVID-19. Depending on resources, pre-test probability, and risk factors for severe disease progression, real-time polymerase chain reaction (RT-PCR) testing may be followed by chest radiography (CXR) or chest computed tomography (CT) to further aid in diagnosis or excluding COVID-19 disease. SARS-CoV-2 has been shown not only to pathologically impact the pulmonary system, but also the cardiovascular, gastrointestinal, and neurological systems to name a few. Imaging has again proven useful in further investigating and managing extrapulmonary disease, with the use of echocardiogram, CT angiography of the cardiovascular and cerebrovascular structures, MRI of the brain, as well as ultrasound of the abdomen and CT of the abdomen and pelvis proving particularly useful. Research in artificial intelligence and its application in the diagnosis of COVID-19 and disease severity prediction is underway, and point-of-care ultrasound is an emerging bedside technique that may allow for more efficient and timely diagnosis of COVID-19.

The outbreak of coronavirus disease (COVID-19) coincided with the season of influenza A pneumonia, a common respiratory infectious disease. Therefore, this study compared ultrasonography and computed tomography (CT) for the diagnosis of the two diseases.

Patients with COVID-19 or influenza A infection hospitalized at our hospital were included. The patients were examined by ultrasonography every day. The CT examination results within 1 day before and after the day of the highest ultrasonography score were selected as the controls. The similarities and differences between the ultrasonography and CT results in the two groups were compared.

There was no difference between the ultrasonography and CT scores (P = .307) for COVID-19, while there was a difference between ultrasonography and CT scores for influenza A pneumonia (P = .024). The ultrasonography score for COVID-19 was higher than that for influenza A pneumonia (P = .000), but there was no difference between the CT scores (P = .830). For both diseases, there was no difference in ultrasonography and CT scores between the left and right lungs; there were differences between the CT scores of the upper and middle lobes, as well as between the upper and lower lobes of the lungs; however, there was no difference between the lower and middle lobes of the lungs.

Ultrasonography is equivalent to the gold standard CT for diagnosing and monitoring the progression of COVID-19. Because of its convenience, ultrasonography has important application value. Furthermore, the diagnostic value of ultrasonography for COVID-19 is higher than that for influenza A pneumonia.

Scanning protocols for lung ultrasound often include 8 or more lung zones, which may limit real-world clinical use. We sought to compare a 2-zone, anterior-superior thoracic ultrasound protocol for B-line artifact detection with an 8-zone approach in patients with known or suspected heart failure using a deep learning (DL) algorithm.

Adult patients with suspected heart failure and B-lines on initial lung ultrasound were enrolled in a prospective observational study. Subjects received daily ultrasounds with a hand-held ultrasound system using an 8-zone protocol (right and left anterior/lateral and superior/inferior). A previously published deep learning algorithm that rates severity of B-lines on a 0-4 scale was adapted for use on hand-held ultrasound full video loops. Average severities for 8 and 2 zones were calculated utilizing DL ratings. Bland-Altman plot analyses were used to assess agreement and identify bias between 2- and 8-zone scores for both primary (all patients, 5728 videos, 205 subjects) and subgroup (confirmed diagnosis of heart failure or pulmonary edema, 4464 videos, 147 subjects) analyses.

Bland-Altman plot analyses revealed excellent agreement for both primary and subgroup analyses. The absolute difference on the 4-point scale between 8- and 2-zone average scores was not significant for the primary dataset (0.03; 95% CI -0.01 to 0.07) or the subgroup (0.01; 95% CI -0.04 to 0.06).

Utilization of a 2-zone, anterior-superior thoracic ultrasound protocol provided similar severity information to an 8-zone approach for a dataset of subjects with known or suspected heart failure.

Lung Ultrasound (LUS) is a reliable, radiation free and bedside imaging technique to assess several pulmonary diseases. Although the diagnosis of COVID-19 is made with the nasopharyngeal swab, detection of pulmonary involvement is key for a safe patient management. LUS is a valid alternative to explore, in paucisymptomatic self-presenting patients, the presence and extension of pneumonia compared to High Resolution Computed Tomography (HRCT) that represent the gold standard. This is a single-centre prospective study with 131 patients enrolled. Twelve lung areas were explored reporting a semiquantitative assessment to obtain the LUS score. Each patient performed reverse-transcription polymerase chain reaction test (rRT-PCR), hemogasanalysis and HRCT. We observed an inverse correlation between LUSs and pO2, P/F, SpO2, AaDO2 (p value < 0.01), a direct correlation with LUSs and AaDO2 (p value < 0.01). Compared with HRCT, LUS showed sensitivity and specificity of 81.8% and 55.4%, respectively, and VPN 75%, VPP 65%. Therefore, LUS can represent an effective alternative tool to detect pulmonary involvement in COVID-19 compared to HRCT.

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

The aim was examination of the intimamedia thickness of carotid arteries in COVID-19 infection.

In 50 patients, the thickness of the intimomedial complex (IMT) in the common carotid arteries was measured. The values were compared with the control group in 2006-9. The condition of the lungs was assessed by ultrasound score (It score) (0-42) as mild (0-14) or mediumsevere (15-28) Covid. IMT thickening risk factors and the value of fibrinogen, IL-6 and CRP were recorded. Two IMT prediction models were formed. The socio-epidemiological model predicts the development of IMT based on epidemiological factors. Apart from these factors, the second model also includes the values of the mentioned biomarkers.

It score 20±6, IMT values right: median 0.99 mm, p25=0.89, p75=1.14; left: 1±0.22 mm. Control: IMTright: median 0.7 mm, p25=0.68 mm; p75=0-9 mm; left: median=0.75 mm, p25=0.6 mm, p75=1.0 mm. The group/control difference is highly significant. Epide mio - logical model: logit (IMT)= 4.463+(2.021+value for GEN)+(0.055x AGE value)+(-3.419x RF value)+(-4.447x SM value)+(5.115x HTA value)+(3.56x DM value)+ (22.389x LIP value)+(24.206x CVD value)+(1.449x other value)+(-0.138x It score value)+(0.19xBMI value). Epidemiological-inflammatory model: logit (IMT)=5.204+ (2.545x GEN value)+(0.076x AGE value)+(-6.132x RF value)+(-7.583x SM value)+(8.744x HTA value)+(6.838x DM value)+(25.446x LIP value)+(28.825x CVD value)+ (2.487x other value)+(-0.218xIt score value)+(0.649x BMI value) +(-0.194x fibrinogen value)+(0.894x IL-6 value)+(0.659x CRP value). Values for both models Exp(B)=4.882; P of sample=0.83; logit=-0.19; OR= 23.84; model accuracy for the first model 87% and for the second 88%; Omnibus test of the first model c2=34.324; p=0.000; reliability coefficient -2LogLH=56.854; Omnibus test of the second model c2=39.774; p=0.000; and -2LogLH=51.403.

The ageing of blood vessels in COVID-19 can be predicted.

The primary diagnostic method of Coronavirus disease 2019 is reverse transcription polymerase chain reaction of the nucleic acid of severe acute respiratory syndrome coronavirus 2 in nasopharyngeal swabs. There is growing evidence regarding the 2019 coronavirus disease imaging results on chest X-rays and computed tomography but the accessibility to standard diagnostic methods may be limited during the pandemic.

Databases used for the search were MEDLINE (PubMed), Scopus Search, and Cochrane Library. The research took into consideration studies published in English until March 2022 and was conducted using the following research query: ((((sars cov [MeSH Terms])) OR (COVID-19)) OR (Sars-Cov2)) OR (Coronavirus)) AND (((((2d echocardiography [MeSH Terms]) OR (doppler ultrasound imaging [MeSH Terms]))) OR (echography [MeSH Terms])) OR (LUS)) OR ("LUNG ULTRASOUND")).

Pulmonary and cardiac ultrasound are cost-effective, widely available, and provide information that can influence management.

Point-of-care ultrasonography is a method that can provide relevant clinical and therapeutic information in patients with COVID-19 where other diagnostic methods may not be easily accessible.

To analyze the diagnostic accuracy of lung ultrasonography (LUS) and high-resolution computed tomography (HRCT), to detect COVID-19.

This study recruited all patients admitted to the emergency medicine unit, due to a suspected COVID-19 infection, during the first wave of the COVID-19 pandemic. These patients also who underwent a standardized LUS examination and a chest HRCT. The signs detected by both LUS and HRCT were reported, as well as the sensitivity, specificity, positive predictive value, and negative predictive value for LUS and HRCT.

This cohort included 159 patients, 101 (63%) were diagnosed with COVID-19. COVID-19 patients showed more often confluent subpleural consolidations and parenchymal consolidations in lower lung regions of LUS. They also had "ground glass" opacities and "crazy paving" on HRCT, while pleural effusion and pulmonary consolidations were more common in non-COVID-19 patients. LUS had a sensitivity of 0.97 (95% CI 0.92-0.99) and a specificity of 0.24 (95% CI 0.07-0.5) for COVID-19 lung infections. HRCT abnormalities resulted in a 0.98 sensitivity (95% CI 0.92-0.99) and 0.1 specificity (95% CI 0.04-0.23) for COVID-19 lung infections.

In this cohort, LUS proved to be a noninvasive, diagnostic tool with high sensitivity for lung abnormalities that were likewise detected by HRCT. Furthermore, LUS, despite its lower specificity, has a high sensitivity for COVID-19, which could prove to be as effective as HRCT in excluding a COVID-19 lung infection.

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.

The novel coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is causing significant morbidity and mortality worldwide. The common presentations in children include involvement of respiratory system leading to pneumonia and acute respiratory distress syndrome, as well as multiorgan dysfunction syndrome and multisystem inflammatory syndrome in children (MIS-C). Pediatric COVID-19 is a milder disease as compared with the adults. Also, there is rise in MIS-C cases which is a hyperinflammatory condition temporally associated with SARS-CoV-2. Since respiratory system is predominantly involved, few of these critically ill children often require respiratory support which can range from simple oxygen delivery devices, high-flow nasal cannula (HFNC), noninvasive ventilation (NIV), invasive mechanical ventilation, and extracorporeal membrane oxygenation (ECMO). Most of the oxygen delivery devices and respiratory interventions generate aerosols and pose risk of transmission of virus to health care providers (HCPs). The use of HFNC and NIV should be limited to children with mild respiratory distress preferably in negative pressure rooms and with adequate personal protective equipment (PPE). However, there should be low thresholds for intubation and invasive mechanical ventilation in the event of clinical deterioration while on any respiratory support. The principle of providing respiratory support requires special droplet and air-borne precautions to limit exposure or transmission of virus to HCPs and at the same time ensuring safety of the patient.

A machine learning method for classifying lung ultrasound is proposed here to provide a point of care tool for supporting a safe, fast, and accurate diagnosis that can also be useful during a pandemic such as SARS-CoV-2. Given the advantages (e.g., safety, speed, portability, cost-effectiveness) provided by the ultrasound technology over other examinations (e.g., X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest public lung ultrasound dataset. Focusing on both accuracy and efficiency, our solution is based on an efficient adaptive ensembling of two EfficientNet-b0 models reaching 100% of accuracy, which, to our knowledge, outperforms the previous state-of-the-art models by at least 5%. The complexity is restrained by adopting specific design choices: ensembling with an adaptive combination layer, ensembling performed on the deep features, and minimal ensemble using two weak models only. In this way, the number of parameters has the same order of magnitude of a single EfficientNet-b0 and the computational cost (FLOPs) is reduced at least by 20%, doubled by parallelization. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where an inaccurate weak model focuses its attention versus an accurate one.

Combined Lung Ultrasound (LUS) and Focused UltraSound for Intensive Care heart (FUSIC Heart - formerly Focused Intensive Care Echocardiography, FICE) can aid diagnosis, risk stratification and management in COVID-19. However, data on its application and results are limited to small studies in varying countries and hospitals. This United Kingdom (UK) national service evaluation study assessed how combined LUS and FUSIC Heart were used in COVID-19 Intensive Care Unit (ICU) patients during the first wave of the pandemic.

Twelve trusts across the UK registered for this prospective study. LUS and FUSIC Heart data were obtained, using a standardised data set including scoring of abnormalities, between 1st February 2020 to 30th July 2020. The scans were performed by intensivists with FUSIC Lung and Heart competency as a minimum standard. Data was anonymised locally prior to transfer to a central database.

372 studies were performed on 265 patients. There was a small but significant relationship between LUS score >8 and 30-day mortality (OR 1.8). Progression of score was associated with an increase in 30-day mortality (OR 1.2). 30-day mortality was increased in patients with right ventricular (RV) dysfunction (49.4% vs 29.2%). Severity of LUS score correlated with RV dysfunction (p < 0.05). Change in management occurred in 65% of patients following a combined scan.

In COVID-19 patients, there is an association between lung ultrasound score severity, RV dysfunction and mortality identifiable by combined LUS and FUSIC Heart. The use of 12-point LUS scanning resulted in similar risk score to 6-point imaging in the majority of cases. Our findings suggest that serial combined LUS and FUSIC Heart on COVID-19 ICU patients may aid in clinical decision making and prognostication.

Lung ultrasound (LUS) has sparked significant interest during COVID-19. LUS is based on the detection and analysis of imaging patterns. Vertical artifacts and consolidations are some of the recognized patterns in COVID-19. However, the interrater reliability (IRR) of these findings has not been yet thoroughly investigated. The goal of this study is to assess IRR in LUS COVID-19 data and determine how many LUS videos and operators are required to obtain a reliable result.

A total of 1035 LUS videos from 59 COVID-19 patients were included. Videos were randomly selected from a dataset of 1807 videos and scored by six human operators (HOs). The videos were also analyzed by artificial intelligence (AI) algorithms. Fleiss' kappa coefficient results are presented, evaluated at both the video and prognostic levels.

Findings show a stable agreement when evaluating a minimum of 500 videos. The statistical analysis illustrates that, at a video level, a Fleiss' kappa coefficient of 0.464 (95% confidence interval [CI] = 0.455-0.473) and 0.404 (95% CI = 0.396-0.412) is obtained for pairs of HOs and for AI versus HOs, respectively. At prognostic level, a Fleiss' kappa coefficient of 0.505 (95% CI = 0.448-0.562) and 0.506 (95% CI = 0.458-0.555) is obtained for pairs of HOs and for AI versus HOs, respectively.

To examine IRR and obtain a reliable evaluation, a minimum of 500 videos are recommended. Moreover, the employed AI algorithms achieve results that are comparable with HOs. This research further provides a methodology that can be useful to benchmark future LUS studies.

Point-of-care (POC) lung ultrasound (LUS) is widely used in the emergency setting and there is an established evidence base across a range of respiratory diseases, including previous viral epidemics. The necessity for rapid testing combined with the limitations of other diagnostic tests has led to the proposal of various potential roles for LUS during the COVID-19 pandemic. This systematic review and meta-analysis focused specifically on the diagnostic accuracy of LUS in adult patients presenting with suspected COVID-19 infection.

Traditional and grey-literature searches were performed on 1 June 2021. Two authors independently carried out the searches, selected studies and completed the Quality Assessment Tool for Diagnostic Test Accuracy Studies (QUADAS-2). Meta-analysis was carried out using established open-source packages in R. We report overall sensitivity, specificity, positive and negative predictive values, and the hierarchical summary receiver operating characteristic curve for LUS. Heterogeneity was determined using the I² statistic.

Twenty studies were included, published between October 2020 and April 2021, providing data from a total of 4314 patients. The prevalence and admission rates were generally high across all studies. Overall, LUS was found to be 87.2% sensitive (95% CI 83.6 to 90.2) and 69.5% specific (95% CI 62.2 to 72.5) and demonstrated overall positive and negative likelihood ratios of 3.0 (95% CI 2.3 to 4.1) and 0.16 (95% CI 0.12 to 0.22), respectively. Separate analyses for each reference standard revealed similar sensitivities and specificities for LUS. Heterogeneity was found to be high across the studies. Overall, the quality of studies was low with a high risk of selection bias due to convenience sampling. There were also applicability concerns because all studies were undertaken during a period of high prevalence.

During a period of high prevalence, LUS had a sensitivity of 87% for the diagnosis of COVID-19 infection. However, more research is required to confirm these results in more generalisable populations, including those less likely to be admitted to hospital.

CRD42021250464.

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.

Lung ultrasound has the potential to enable standardized follow-up without radiation exposure and with lower associated costs in comparison to CT scans. It is a valuable tool to follow up on patients after a COVID-19 infection and evaluate if there is pulmonary fibrosis developing. Echocardiography, including strain imaging, is a proven tool to assess various causes of dyspnea and adds valuable information in the context of long COVID care. Including two-dimensional (2D) strain imaging, a better comprehension of myocardial damage in post-COVID syndrome can be made. Especially 2D strain imaging (left and the right ventricular strain) can provide information about prognosis.

 Lung ultrasound (LUS) is a reliable, radiation-free, and bedside imaging technique used to assess several pulmonary diseases. Although COVID-19 is diagnosed with a nasopharyngeal swab, detection of pulmonary involvement is crucial for safe patient discharge. Computed tomography (CT) is currently the gold standard. To treat paucisymptomatic patients, we have implemented a "fast track" pathway in our emergency department, using LUS as a valid alternative. Minimal data is available in the literature about interobserver reliability and the level of expertise needed to perform a reliable examination. Our aim was to assess these.

 This was a single-center prospective study. We enrolled 96 patients. 12 lung areas were explored in each patient with a semiquantitative assessment of pulmonary aeration loss in order to obtain the LUS score. Scans were performed by two different operators, an expert and a novice, who were blinded to their colleague's results.

 96 patients were enrolled. The intraclass correlation coefficient (ICC) showed excellent agreement between the expert and the novice operator (ICC 0.975; 0.962-0.983); demographic features (age, sex, and chronic pulmonary disease) did not influence the reproducibility of the method. The ICC was 0.973 (0.950-0.986) in males, 0.976 (0.959-0.986) in females; 0.965 (0.940-0.980) in younger patients (≤ 46 yrs), and 0.973 (0.952-0.985) in older (> 46 yrs) patients. The ICC was 0.967 (0.882-0.991) in patients with pulmonary disease and 0.975 (0.962-0.984) in the other patients. The learning curve showed an increase in interobserver agreement.

 Our results confirm the feasibility and reproducibility of the method among operators with different levels of expertise, with a rapid learning curve.

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.

In the field of biomedical imaging, ultrasonography has become common practice, and used as an important auxiliary diagnostic tool with unique advantages, such as being non-ionizing and often portable. This article reviews the state-of-the-art in medical ultrasound (US) image processing and in particular its applications in the examination of the lungs. First, we briefly introduce the basis of lung US (LUS) examination. We focus on (i) the characteristics of lung ultrasonography and (ii) its ability to detect a variety of diseases through the identification of various artifacts exhibiting on LUS images. We group medical US image computing methods into two categories: 1) model-based methods and 2) data-driven methods. We particularly discuss inverse problem-based methods exploited in US image despeckling, deconvolution, and line artifacts detection for the former, while we exemplify various works based on deep/machine learning (ML), which exploit various network architectures through supervised, weakly supervised, and unsupervised learning for the data-driven approaches.

COVID-19 raised the need for automatic medical diagnosis, to increase the physicians' efficiency in managing the pandemic. Among all the techniques for evaluating the status of the lungs of a patient with COVID-19, lung ultrasound (LUS) offers several advantages: portability, cost-effectiveness, safety. Several works approached the automatic detection of LUS imaging patterns related COVID-19 by using deep neural networks (DNNs). However, the decision processes based on DNNs are not fully explainable, which generally results in a lack of trust from physicians. This, in turn, slows down the adoption of such systems. In this work, we use two previously built DNNs as feature extractors at the frame level, and automatically synthesize, by means of an evolutionary algorithm, a decision tree (DT) that aggregates in an interpretable way the predictions made by the DNNs, returning the severity of the patients' conditions according to a LUS score of prognostic value. Our results show that our approach performs comparably or better than previously reported aggregation techniques based on an empiric combination of frame-level predictions made by DNNs. Furthermore, when we analyze the evolved DTs, we discover properties about the DNNs used as feature extractors. We make our data publicly available for further development and reproducibility.

To evaluate the role of lung ultrasound (LUS) in recognizing lung abnormalities in pregnant women affected by COVID-19 pneumonia.

An observational study analyzing LUS patterns in 60 consecutively enrolled pregnant women affected by COVID-19 infection was performed. LUS was performed by using a standardized protocol by Soldati et al. The scoring system of LUS findings ranged from 0 to 3 in increasing alteration severity. The highest score obtained from each landmark was reported and the sum of the 12 zones examined was calculated.

Patients were divided into two groups: 26 (43.3%) patients with respiratory symptoms and 32 (53.3%) patients without respiratory symptoms; 2 patients were asymptomatic (3.3%). Among the patients with respiratory symptoms 3 (12.5%) had dyspnea that required a mild Oxygen therapy. A significant correlation was found between respiratory symptoms and LUS score (p < 0.001) and between gestational weeks and respiratory symptoms (p = 0.023). Regression analysis showed that age and respiratory symptoms were risk factors for highest LUS score (p < 0.005).

LUS can affect the clinical decision course and can help in stratifying patients according to its findings. The lack of ionizing radiation and its repeatability makes it a reliable diagnostic tool in the management of pregnant women.

(1) Background: Millions of people worldwide were infected with COVID-19. After the acute phase of the disease, many suffer from prolonged symptoms, the post-COVID syndrome, especially the phenotype with lung residuals. Many open questions regarding lung ultrasound (LUS) have to be answered. One essential question is the means for optimal following-up of patients with post-COVID-19 residuals with LUS; (2) Methods: A retrospective data analysis of patients after acute COVID-19 infection diagnosed with post-COVID syndrome in the state hospital of Steyr and the rehabilitation center of Hochegg was performed. LUS examinations following a 12-zone scanning protocol were performed, and the LUS score quantified comet tail artifacts. A total of 16 patients were evaluated twice with LUS from May 2020 until June 2021. (3) Results: All patients’ reverberation artifacts were reduced over time. The initial LUS score of 17.75 (SD 4.84) points was decreased over the duration of the second rehabilitation to 8,2 (SD 5.94). The difference in the Wilcoxon test was significant (p < 0.001); (4) Conclusions: Lung ultrasound was a valuable tool in the follow-up of post-COVID-syndrome with lung residuals in the first wave of COVID-19. A reduction in reverberation artifacts was demonstrated. Further studies about the clinical significance have to follow.

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.