An update on the role of ultrasound lung artifacts in the diagnosis of respiratory diseases.

Introduction: Lung ultrasound has consolidated over the years its valuable role in supporting routine clinical activity in different settings. Every disease that alters peripheral airspace geometry can generate a superficial structure with low impedance mismatch which can be capable of trapping ultrasound waves within reflective interfaces.

Areas Covered: Qualitative approaches to the description of horizontal and vertical acoustic artifacts have been adopted by physicians for a long time with the consequence of poor diagnostic accuracy and reproducibility.

Potential novel role of asthma biologics as rescue therapy in the intensive care unit for life-threatening asthma exacerbations: a systematic review.

Severe asthma exacerbations have high morbidity and mortality. The management can be challenging, and the optimal strategy for patients admitted to the intensive care unit (ICU) with life-threatening and near-fatal asthma has not been fully defined. An interesting area of research is represented by the rescue or compassionate use of biological drugs when all treatments fail, including advanced interventions such as extracorporeal membrane oxygenation.

CHEst PHysical Examination integrated with UltraSound - Phase (CHEPHEUS1). A survey of Accademia di Ecografia Toracica (AdET).

Background: Chest physical exam (CPE) is based on the four pillars of classical semiotics. However, CPE's sensitivity and specificity are low, and is affected by operators' skills. The aim of this work was to explore the contribution of chest ultrasound (US) to the traditional CPE.

Synthetic Lung Ultrasound Data Generation Using Autoencoder With Generative Adversarial Network.

Class imbalance is a significant challenge in medical image analysis, particularly in lung ultrasound (LUS), where severe patterns are often underrepresented. Traditional oversampling techniques, which simply duplicate original data, have limited effectiveness in addressing this issue. To overcome these limitations, this study introduces a novel supervised autoencoder generative adversarial network (SA-GAN) for data augmentation, leveraging advanced generative artificial intelligence (AI) to create high-quality synthetic samples for minority classes.

Guidelines for the use of lung ultrasound to optimise the management of neonatal respiratory distress: international expert consensus.

Background: Respiratory distress is the main reason for the admission of infants to the neonatal intensive care unit (NICU). Rapid identification of the causes of respiratory distress and selection of appropriate and effective treatment strategies are important to optimise favourable short- and long-term patient outcomes. Lung ultrasound (LUS) technology has become increasingly important in this field.

Ultrasound and Intrapleural Enzymatic Therapy for Complicated Pleural Effusion: A Case Series with a Literature Review.

Pleural effusion is the most common manifestation of pleural disease, and chest ultrasound is crucial for diagnostic workup and post-treatment monitoring. Ultrasound helps distinguish the various types of pleural effusion and enables the detection of typical manifestations of empyema, which presents as a complicated, septated effusion. This may benefit from drainage and the use of intrapleural enzyme therapy or may require more invasive approaches, such as medical or surgical thoracoscopy.

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

Over the last 20 years, scientific literature and interest on chest/lung ultrasound (LUS) have exponentially increased. Interpreting mixed-anatomical and artifactual-pictures determined the need of a proposal of a new nomenclature of artifacts and signs to simplify learning, spread, and implementation of this technique. The aim of this review is to collect and analyze different signs and artifacts reported in the history of chest ultrasound regarding normal lung, pleural pathologies, and lung consolidations.

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

Since the outbreak of COVID-19, efforts have been made towards semi-quantitative analysis of lung ultrasound (LUS) data to assess the patient's condition. Several methods have been proposed in this regard, with a focus on frame-level analysis, which was then used to assess the condition at the video and prognostic levels. However, no extensive work has been done to analyze lung conditions directly at the video level.

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

Automated ultrasound imaging assessment of the effect of CoronaVirus disease 2019 (COVID-19) on lungs has been investigated in various studies using artificial intelligence-based (AI) methods. However, an extensive analysis of state-of-the-art Convolutional Neural Network-based (CNN) models for frame-level scoring, a comparative analysis of aggregation techniques for video-level scoring, together with a thorough evaluation of the capability of these methodologies to provide a clinically valuable prognostic-level score is yet missing within the literature. In addition to that, the impact on the analysis of the posterior probability assigned by the network to the predicted frames as well as the impact of temporal downsampling of LUS data are topics not yet extensively investigated.

Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees.

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).

Possible Role of Chest Ultrasound in the Assessment of Costo-Phrenic Angle Lesions Prior to Medical Thoracoscopy: A Retrospective Pilot Case Series.

Background: Pleural malignancy (PM) and malignant pleural effusion (MPE) represent an increasing burden of diseases. Costo-phrenic angle (CPA) could be involved by malignant small nodularities or thickenings in the case of MPE. The aim of this study was to evaluate whether lung ultrasound (LUS), performed prior to medical thoracoscopy (MT), could detect pleural abnormalities in CPA not easily detectable by chest computed tomography scan (CCT).

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

Although during the last few years the lung ultrasound (LUS) technique has progressed substantially, several artifacts, which are currently observed in clinical practice, still need a solid explanation of the physical phenomena involved in their origin. This is particularly true for vertical artifacts, conventionally known as B-lines, and for their use in clinical practice. A wider consensus and a deeper understanding of the nature of these artifactual phenomena will lead to a better classification and a shared nomenclature, and, ultimately, result in a more objective correlation between anatomo-pathological data and clinical scenarios.

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

Lung ultrasound (LUS) has been increasingly expanding since the 1990s, when the clinical relevance of vertical artifacts was first reported. However, the massive spread of LUS is only recent and is associated with the coronavirus disease 2019 (COVID-19) pandemic, during which semi-quantitative computer-aided techniques were proposed to automatically classify LUS data. In this review, we discuss the state of the art in LUS, from semi-quantitative image analysis approaches to quantitative techniques involving the analysis of radiofrequency data.

New International Guidelines and Consensus on the Use of Lung Ultrasound.

Following the innovations and new discoveries of the last 10 years in the field of lung ultrasound (LUS), a multidisciplinary panel of international LUS experts from six countries and from different fields (clinical and technical) reviewed and updated the original international consensus for point-of-care LUS, dated 2012. As a result, a total of 20 statements have been produced. Each statement is complemented by guidelines and future developments proposals.

Human-to-AI Interrater Agreement for Lung Ultrasound Scoring in COVID-19 Patients.

Objectives: 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.

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

The application of lung ultrasound (LUS) imaging for the diagnosis of lung diseases has recently captured significant interest within the research community. With the ongoing COVID-19 pandemic, many efforts have been made to evaluate LUS data. A four-level scoring system has been introduced to semiquantitatively assess the state of the lung, classifying the patients.

Investigating training-test data splitting strategies for automated segmentation and scoring of COVID-19 lung ultrasound images.

Ultrasound in point-of-care lung assessment is becoming increasingly relevant. This is further reinforced in the context of the COVID-19 pandemic, where rapid decisions on the lung state must be made for staging and monitoring purposes. The lung structural changes due to severe COVID-19 modify the way ultrasound propagates in the parenchyma.

Lung Ultrasound in COVID-19 and Post-COVID-19 Patients, an Evidence-Based Approach.

Objectives: Worldwide, lung ultrasound (LUS) was utilized to assess coronavirus disease 2019 (COVID-19) patients. Often, imaging protocols were however defined arbitrarily and not following an evidence-based approach. Moreover, extensive studies on LUS in post-COVID-19 patients are currently lacking.

Integrating Domain Knowledge Into Deep Networks for Lung Ultrasound With Applications to COVID-19.

Lung ultrasound (LUS) is a cheap, safe and non-invasive imaging modality that can be performed at patient bed-side. However, to date LUS is not widely adopted due to lack of trained personnel required for interpreting the acquired LUS frames. In this work we propose a framework for training deep artificial neural networks for interpreting LUS, which may promote broader use of LUS.

Deep learning applied to lung ultrasound videos for scoring COVID-19 patients: A multicenter study.

In the current pandemic, lung ultrasound (LUS) played a useful role in evaluating patients affected by COVID-19. However, LUS remains limited to the visual inspection of ultrasound data, thus negatively affecting the reliability and reproducibility of the findings. Moreover, many different imaging protocols have been proposed, most of which lacked proper clinical validation.