Over the last 20 years, scientific literature and interest on chest/lung ultrasound (LUS) have exponentially increased. Interpreting mixed-anatomical and artifactual-pictures determined the need of a proposal of a new nomenclature of artifacts and signs to simplify learning, spread, and implementation of this technique. The aim of this review is to collect and analyze different signs and artifacts reported in the history of chest ultrasound regarding normal lung, pleural pathologies, and lung consolidations. By reviewing the possible physical and anatomical interpretation of these artifacts and signs reported in the literature, this work aims to present the AdET (Accademia di Ecografia Toracica) proposal of nomenclature and to bring order between published studies.
Lung Diseases , Lung , Humans , Lung/diagnostic imaging , Lung Diseases/diagnostic imaging , Thorax , Ultrasonography/methods , Artifacts
Thoracic ultrasound is an important diagnostic tool employed by many clinicians in well-defined applications [...].
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.
Artificial Intelligence , COVID-19 , Humans , Prognosis , Benchmarking , Ultrasonography
BACKGROUND: The original observation that lung ultrasound provides information regarding the physical state of the organ, rather than the anatomical details related to the disease, has reinforced the idea that the observed acoustic signs represent artifacts. However, the definition of artifact does not appear adequate since pulmonary ultrasound signs have shown valuable diagnostic accuracy, which has been usefully exploited by physicians in numerous pathologies. METHOD: A specific method has been used over the years to analyze lung ultrasound data and to convert artefactual information into anatomical information. RESULTS: A physical explanation of the genesis of the acoustic signs is provided, and the relationship between their visual characteristics and the surface histopathology of the lung is illustrated. Two important sources of potential signal alteration are also highlighted. CONCLUSIONS: The acoustic signs are generated by acoustic traps that progressively release previously trapped energy. Consequently, the acoustic signs highlight the presence of acoustic traps and quantitatively describe their distribution on the lung surface; they are not artifacts, but pathology footprints and anatomical information. Moreover, the impact of the dynamic focusing algorithms and the impact of different probes on the visual aspect of the acoustic signs should not be neglected.
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.
Artifacts , Lung , Humans , Lung/diagnostic imaging , Syndrome , Ultrasonography
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.
COVID-19 , Humans , SARS-CoV-2 , Consensus , Lung/diagnostic imaging , Point-of-Care Testing , Ultrasonography
Purpose: We aimed to assess the role of lung ultrasound (LUS) in the diagnosis and prognosis of SARS-CoV-2 pneumonia, by comparing it with High Resolution Computed Tomography (HRCT). Patients and methods: All consecutive patients with laboratory-confirmed SARS-CoV-2 infection and hospitalized in COVID Centers were enrolled. LUS and HRCT were carried out on all patients by expert operators within 48−72 h of admission. A four-level scoring system computed in 12 regions of the chest was used to categorize the ultrasound imaging, from 0 (absence of visible alterations with ultrasound) to 3 (large consolidation and cobbled pleural line). Likewise, a semi-quantitative scoring system was used for HRCT to estimate pulmonary involvement, from 0 (no involvement) to 5 (>75% involvement for each lobe). The total CT score was the sum of the individual lobar scores and ranged from 0 to 25. LUS scans were evaluated according to a dedicated scoring system. CT scans were assessed for typical findings of COVID-19 pneumonia (bilateral, multi-lobar lung infiltration, posterior peripheral ground glass opacities). Oxygen requirement and mortality were also recorded. Results: Ninety-nine patients were included in the study (male 68.7%, median age 71). 40.4% of patients required a Venturi mask and 25.3% required non-invasive ventilation (C-PAP/Bi-level). The overall mortality rate was 21.2% (median hospitalization 30 days). The median ultrasound thoracic score was 28 (IQR 20−36). For the CT evaluation, the mean score was 12.63 (SD 5.72), with most of the patients having LUS scores of 2 (59.6%). The bivariate correlation analysis displayed statistically significant and high positive correlations between both the CT and composite LUS scores and ventilation, lactates, COVID-19 phenotype, tachycardia, dyspnea, and mortality. Moreover, the most relevant and clinically important inverse proportionality in terms of P/F, i.e., a decrease in P/F levels, was indicative of higher LUS/CT scores. Inverse proportionality P/F levels and LUS and TC scores were evaluated by univariate analysis, with a P/F−TC score correlation coefficient of −0.762, p < 0.001, and a P/F−LUS score correlation coefficient of −0.689, p < 0.001. Conclusions: LUS and HRCT show a synergistic role in the diagnosis and disease severity evaluation of COVID-19.
There is increasing recognition of the role of lung ultrasound (LUS) to assess bronchiolitis severity in children. However, available studies are limited to small, single-center cohorts. We aimed to assess a qualitative and quantitative LUS protocol to evaluate the course of bronchiolitis at diagnosis and during follow-up. This is a prospective, multicenter study. Children with bronchiolitis were stratified according to clinical severity and underwent four LUS evaluations at set intervals. LUS was classified according to four models: (1) positive/negative; (2) main LUS pattern (normal/interstitial/consolidative/mixed) (3) LUS score; (4) LUS score with cutoff. Two hundred and thirty-three children were enrolled. The baseline LUS was significantly associated with bronchiolitis severity, using both the qualitative (positive/negative LUS p < 0.001; consolidated/normal LUS pattern or mixed/normal LUS p < 0.001) and quantitative models (cutoff score > 9 p < 0.001; LUS mean score p < 0.001). During follow-up, all LUS results according to all LUS models improved (p < 0.001). Better cut off value was declared at a value of >9 points. Conclusions: Our study supports the role of a comprehensive qualitative and quantitative LUS protocol for the identification of severe cases of bronchiolitis and provides data on the evolution of lung aeration during follow-up.
In lung ultrasound (LUS), the interactions between the acoustic pulse and the lung surface (including the pleura and a small subpleural layer of tissue) are crucial. Variations of the peripheral lung density and the subpleural alveolar shape and its configuration are typically connected to the presence of ultrasound artifacts and consolidations. COVID-19 pneumonia can give rise to a variety of pathological pulmonary changes ranging from mild diffuse alveolar damage (DAD) to severe acute respiratory distress syndrome (ARDS), characterized by peripheral bilateral patchy lung involvement. These findings are well described in CT imaging and in anatomopathological cases. Ultrasound artifacts and consolidations are therefore expected signs in COVID-19 pneumonia because edema, DAD, lung hemorrhage, interstitial thickening, hyaline membranes, and infiltrative lung diseases when they arise in a subpleural position, generate ultrasound findings. This review analyzes the structure of the ultrasound images in the normal and pathological lung given our current knowledge, and the role of LUS in the diagnosis and monitoring of patients with COVID-19 lung involvement.
For over 15 years, thoracic ultrasound has been applied in the evaluation of numerous lung diseases, demonstrating a variable diagnostic predictive power compared to traditional imaging techniques such as chest radiography and CT. However, in unselected pulmonary patients, there are no rigorous scientific demonstrations of the complementarity of thoracic ultrasound with traditional and standardized imaging techniques that use radiation. In this study 101 unselected pulmonary patients were evaluated blindly with ultrasound chest examinations during their hospital stay. Other instrumental examinations, carried out during hospitalization, were standard chest radiography, computed tomography (CT), and, when needed, radioisotopic investigation and cardiac catheterization. The operator who performed the ultrasound examinations was unaware of the anamnestic and clinical data of the patients. Diffuse fibrosing disease was detected with a sensitivity, specificity and diagnostic accuracy of 100%, 95% and 97%, respectively. In pleural effusions, ultrasound showed a sensitivity, specificity and diagnostic accuracy of 100%. In consolidations, the sensitivity, specificity and diagnostic accuracy were 83%, 98% and 93%, respectively. Low values of sensitivity were recorded for surface nodulations of less than one centimeter. Isolated subpleural ground glass densities were identified as White Lung with a sensitivity of 72% and a specificity of 86%. Only the associations Diffuse ultrasound findings/Definitive fibrosing disease, Ultrasound Consolidation/Definitive consolidation and non-diffuse ultrasound artefactual features/Definitive vascular pathology (pulmonary hypertension, embolism) were statistically significant with adjusted residuals of 7.9, 7 and 4.1, respectively. The obtained results show how chest ultrasound is an effective complementary diagnostic tool for the pulmonologist. When performed, as a complement to the patient's physical examination, it can restrict the diagnostic hypothesis in the case of pleural effusion, consolidation and diffuse fibrosing disease of the lung.
INTRODUCTION: Vertical artifacts, including B lines, are frequently seen in a variety of lung diseases. Their sonomorphology varies in length, width, shape, and internal reverberations. The reason for this diversity is still unknown and is the cause of discussion between clinicians and ultrasound physics engineers. AIM: The aim of this work is to sum up the most common clinician observations and provide an explanation to each of them derived from ultrasound physics. MATERIALS AND METHODS: Based on clinical and engineering experiences as well as data collected from relevant literature, the sonomorphology of vertical artifacts was analyzed. Thirteen questions and answers were prepared on the common sonomorphology of vertical artifacts, current nomenclature, and clinical observations. CONCLUSIONS: From a clinical standpoint, the analysis of vertical artifacts is very important and requires that further clinical studies be conducted in cooperation with engineers who specialize in physics.
With the emergence of the Covid-19 pandemic, pleuropulmonary ultrasound has become a very common tool in clinical practice, even in the pediatric field. Therefore, the clinicians' need to speak a common ultrasound language becomes increasingly necessary. The Italian scientific society AdET (Academy of Thoracic Ultrasound) has been carrying out the study and dissemination of pulmonary ultrasound in medical practice in Italy for years. With this article, the pediatric AdET group wants to propose a report model of pediatric pulmonary ultrasound as a useful tool in daily clinical practice to interpret the images and reach a diagnostic conclusion, aiming to share a standardized approach that may also support the sharing of research findings.
COVID-19 , Pediatrics , Child , Humans , Lung/diagnostic imaging , Pandemics , Ultrasonography
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. When using LUS to evaluate a patient's condition, both anatomical phenomena (e.g., the pleural line, presence of consolidations), as well as sonographic artifacts (such as A- and B-lines) are of importance. In our framework, we integrate domain knowledge into deep neural networks by inputting anatomical features and LUS artifacts in the form of additional channels containing pleural and vertical artifacts masks along with the raw LUS frames. By explicitly supplying this domain knowledge, standard off-the-shelf neural networks can be rapidly and efficiently finetuned to accomplish various tasks on LUS data, such as frame classification or semantic segmentation. Our framework allows for a unified treatment of LUS frames captured by either convex or linear probes. We evaluated our proposed framework on the task of COVID-19 severity assessment using the ICLUS dataset. In particular, we finetuned simple image classification models to predict per-frame COVID-19 severity score. We also trained a semantic segmentation model to predict per-pixel COVID-19 severity annotations. Using the combined raw LUS frames and the detected lines for both tasks, our off-the-shelf models performed better than complicated models specifically designed for these tasks, exemplifying the efficacy of our framework.
COVID-19 , COVID-19/diagnostic imaging , Humans , Lung/diagnostic imaging , Neural Networks, Computer , SARS-CoV-2 , Ultrasonography/methods
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. To address these problems, we were the first to propose a standardized imaging protocol and scoring system. Next, we developed the first deep learning (DL) algorithms capable of evaluating LUS videos providing, for each video-frame, the score as well as semantic segmentation. Moreover, we have analyzed the impact of different imaging protocols and demonstrated the prognostic value of our approach. In this work, we report on the level of agreement between the DL and LUS experts, when evaluating LUS data. The results show a percentage of agreement between DL and LUS experts of 85.96% in the stratification between patients at high risk of clinical worsening and patients at low risk. These encouraging results demonstrate the potential of DL models for the automatic scoring of LUS data, when applied to high quality data acquired accordingly to a standardized imaging protocol.
COVID-19 , Deep Learning , Humans , Lung/diagnostic imaging , Reproducibility of Results , SARS-CoV-2 , Ultrasonography
BACKGROUND: This study concerns the application of lung ultrasound (LUS) for the evaluation of the significance of vertical artifact changes with frequency and pleural line abnormalities in differentiating pulmonary edema from pulmonary fibrosis. STUDY DESIGN AND METHODS: The study was designed as a diagnostic test. Having qualified patients for the study, an ultrasound examination was performed, consistent with a predetermined protocol, and employing convex and linear transducers. We investigated the possibility of B-line artifact conversion depending on the set frequency (2 MHz and 6 MHz), and examined pleural line abnormalities. RESULTS: The study group comprised 32 patients with interstitial lung disease (ILD) (and fibrosis) and 30 patients with pulmonary edema. In total, 1941 cineloops were obtained from both groups and analyzed. The employment of both types of transducers (linear and convex) was most effective (specificity 91%, specificity 97%, positive predictive value (PPV) 97%, negative predictive value (NPV) 91%, LR(+) 27,19, LR(-) 0.097, area under curve (AUC) = 0.936, p = 7 × 10-6). INTERPRETATION: The best accuracy in differentiating the etiology of B-line artifacts was obtained with the use of both types of transducers (linear and convex), complemented with the observation of the conversion of B-line artifacts to Z-line.
The analysis of vertical reverberation artefacts is an essential component of the differential diagnosis in pulmonary ultra-sound. Traditionally, they are often, but not exclusively, called B-line artefacts (BLA) and/or comet tail artefacts (CTA), but this view is misleading. In this position paper we clarify the terminology and relation of the two lung reverberation artefacts BLA and CTA to spe-cific clinical scenarios. BLA are defined by a normal pleura line and are a typical hallmark of cardiogenic pulmonary edema after exclusion of certain pathologies including pneumonia or lung contusion, whereas CTAs show an irregular pleura line representing a variety of parenchymal lung diseases. The dual approach using low frequency transducers to determine BLA and high frequency transducer to determine the pleural surface is recommended.
Lung Diseases , Pulmonary Edema , Ultrasonography , Artifacts , Humans , Lung/diagnostic imaging , Lung Diseases/diagnostic imaging
