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. 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

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

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

A two-dimensional angular interpolation based on radial basis functions for high frame rate ultrafast imaging.

To solve the problem of reduced image quality in plane wave imaging (PWI), coherent plane wave compounding (CPWC) has been introduced, based on a combination of plane wave images from several directions (i.e., with different angles). However, the number of angles needed to reach a reasonable image quality affects the maximum achievable frame rate in CPWC. In this study, we suggest reducing the tradeoff between the image quality and the frame rate in CPWC by employing two-dimensional (2D) interpolation based on radial basis functions. More specifically, we propose constructing a three-dimensional spatio-angular structure to integrate both spatial and angular information into the reconstruction prior to 2D interpolation. The rationale behind our proposal is to reduce the number of transmissions and then apply the 2D interpolation along the angle dimension to reconstruct the missing information corresponding to the angles not selected for CPWC imaging. To evaluate the proposed technique, we applied it to the PWI challenges in the medical ultrasound database. Results show that we can achieve 3× to 4× improvement in frame rate while maintaining acceptable image quality compared to the case of using all the angles.

Best Practice Recommendations for the Safe use of Lung Ultrasound.

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

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). 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.

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. This study analyses the impact of different LUS imaging protocols on the evaluation of COVID-19 and post-COVID-19 LUS data. METHODS: LUS data from 220 patients were collected, 100 COVID-19 positive and 120 post-COVID-19. A validated and standardized imaging protocol based on 14 scanning areas and a 4-level scoring system was implemented. We utilized this dataset to compare the capability of 5 imaging protocols, respectively based on 4, 8, 10, 12, and 14 scanning areas, to intercept the most important LUS findings. This to evaluate the optimal trade-off between a time-efficient imaging protocol and an accurate LUS examination. We also performed a longitudinal study, aimed at investigating how to eventually simplify the protocol during follow-up. Additionally, we present results on the agreement between AI models and LUS experts with respect to LUS data evaluation. RESULTS: A 12-areas protocol emerges as the optimal trade-off, for both COVID-19 and post-COVID-19 patients. For what concerns follow-up studies, it appears not to be possible to reduce the number of scanning areas. Finally, COVID-19 and post-COVID-19 LUS data seem to show differences capable to confuse AI models that were not trained on post-COVID-19 data, supporting the hypothesis of the existence of LUS patterns specific to post-COVID-19 patients. CONCLUSIONS: A 12-areas acquisition protocol is recommended for both COVID-19 and post-COVID-19 patients, also during follow-up.

On the Impact of Different Lung Ultrasound Imaging Protocols in the Evaluation of Patients Affected by Coronavirus Disease 2019: How Many Acquisitions Are Needed?

Lung ultrasound (LUS) is currently being extensively used for the evaluation of patients affected by coronavirus disease 2019. In the past months, several imaging protocols have been proposed in the literature. However, how the different protocols would compare when applied to the same patients had not been investigated yet. To this end, in this multicenter study, we analyzed the outcomes of 4 different LUS imaging protocols, respectively based on 4, 8, 12, and 14 LUS acquisitions, on data from 88 patients. Results show how a 12-area acquisition system seems to be a good tradeoff between the acquisition time and accuracy.

Lung Ultrasound for COVID-19 Patchy Pneumonia: Extended or Limited Evaluations?

OBJECTIVES: The 2019 novel coronavirus (severe acute respiratory syndrome coronavirus 2) is causing cases of severe pneumonia. Lung ultrasound (LUS) could be a useful tool for physicians detecting a bilateral heterogeneous patchy distribution of pathologic findings in a symptomatic suggestive context. The aim of this study was to focus on the implications of limiting LUS examinations to specific regions of the chest. METHODS: Patients were evaluated with a standard sequence of LUS scans in 14 anatomic areas. A scoring system of LUS findings was reported, ranging from 0 to 3 (worst score, 3). The scores reported on anterior, lateral, and posterior landmarks were analyzed separately and compared with each other and with the global findings. RESULTS: Thirty-eight patients were enrolled. A higher prevalence of score 0 was observed in the anterior region (44.08%). On the contrary, 21.05% of posterior regions and 13.62% of lateral regions were evaluated as score 3, whereas only 5.92% of anterior regions were classified as score 3. Findings from chest computed tomography performed in 16 patients with coronavirus disease 2019 correlated with and matched the distribution of findings from LUS. CONCLUSIONS: To assess the quantity and severity of lung disease, a comprehensive LUS examination is recommended. Omitting areas of the chest misses involved lung.

A New Lung Ultrasound Protocol Able to Predict Worsening in Patients Affected by Severe Acute Respiratory Syndrome Coronavirus 2 Pneumonia.

OBJECTIVES: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can generate severe pneumonia associated with high mortality. A bedside lung ultrasound (LUS) examination has been shown to have a potential role in this setting. The purpose of this study was to evaluate the potential prognostic value of a new LUS protocol (evaluation of 14 anatomic landmarks, with graded scores of 0-3) in patients with SARS-CoV-2 pneumonia and the association of LUS patterns with clinical or laboratory findings. METHODS: A cohort of 52 consecutive patients with laboratory-confirmed SARS-CoV-2 underwent LUS examinations on admission in an internal medicine ward and before their discharge. A total LUS score as the sum of the scores at each explored area was computed. We investigated the association between the LUS score and clinical worsening, defined as a combination of high-flow oxygen support, intensive care unit admission, or 30-day mortality as the primary end point. RESULTS: Twenty (39%) patients showed a worse outcome during the observation period; the mean LUS scores ± SDs were 20.4 ± 8.5 and 29.2 ± 7.3 in patients without and with worsening, respectively (P < .001). In a multivariable analysis, adjusted for comorbidities (>2), age (>65 years), sex (male), and body mass index (≥25 kg/m2 ), the association between the LUS score and worsening (odds ratio, 1.17; 95% confidence interval, 1.05 to 1.29; P = .003) was confirmed, with good discrimination of the model (area under the receiver operating characteristic curve, 0.82). A median LUS score higher than 24 was associated with an almost 6-fold increase in the odds of worsening (odds ratio, 5.67; 95% confidence interval, 1.29 to 24.8; P = .021). CONCLUSIONS: Lung ultrasound can represent an effective tool for monitoring and stratifying the prognosis of patients with SARS-CoV-2 pulmonary involvement.

Dependence of lung ultrasound vertical artifacts on frequency, bandwidth, focus and angle of incidence: An in vitro study.

Lung ultrasound (LUS) is nowadays widely adopted by clinicians to evaluate the state of the lung surface. However, being mainly based on the evaluation of vertical artifacts, whose genesis is still unclear, LUS is affected by qualitative and subjective analyses. Even though semi-quantitative approaches supported by computer aided methods can reduce subjectivity, they do not consider the dependence of vertical artifacts on imaging parameters, and could not be classified as fully quantitative. They are indeed mainly based on scoring LUS images, reconstructed with standard clinical scanners, through the sole evaluation of visual patterns, whose visualization depends on imaging parameters. To develop quantitative techniques is therefore fundamental to understand which parameters influence the vertical artifacts' intensity. In this study, we quantitatively analyzed the dependence of nine vertical artifacts observed in a thorax phantom on four parameters, i.e., center frequency, focal point, bandwidth, and angle of incidence. The results showed how the vertical artifacts are significantly affected by these four parameters, and confirm that the center frequency is the most impactful parameter in artifacts' characterization. These parameters should hence be carefully considered when developing a LUS quantitative approach.

Introduction to the special issue on lung ultrasound.

The potential of lung ultrasound (LUS) has become manifest in the light of the recent COVID-19 pandemic. The need for a point-of care, quantitative, and widely available assessment of lung condition is critical. However, conventional ultrasound imaging was never designed for lung assessment. This limits LUS to the subjective and qualitative interpretation of artifacts and imaging patterns visible on ultrasound images. A number of research groups have begun to tackle this limitation, and this special issue reports on their most recent findings. Through in silico, in vitro, and in vivo studies (preclinical animal studies and pilot clinical studies on human subjects), the research presented aims at understanding and modelling the physical phenomena involved in ultrasound propagation, and at leveraging these phenomena to extract semi-quantitative and quantitative information relevant to estimate changes in lung structure. These studies are the first steps in unlocking the full potential of lung ultrasound as a relevant tool for lung assessment.

Numerical study on lung ultrasound B-line formation as a function of imaging frequency and alveolar geometries.

Lung ultrasound (LUS) has become a widely adopted diagnostic method for several lung diseases. However, the presence of air inside the lung does not allow the anatomical investigation of the organ. Therefore, LUS is mainly based on the interpretation of vertical imaging artifacts, called B-lines. These artifacts correlate with several pathologies, but their genesis is still partly unknown. Within this framework, this study focuses on the factors affecting the artifacts' formation by numerically simulating the ultrasound propagation within the lungs through the toolbox k-Wave. Since the main hypothesis behind the generation of B-lines relies on multiple scattering phenomena occurring once acoustic channels open at the lung surface, the impact of changing alveolar size and spacing is of interest. The tested domain is of size 4 cm × 1.6 cm, the investigated frequencies vary from 1 to 5 MHz, and the explored alveolar diameters and spacing range from 100 to 400 µm and from 20 to 395 µm, respectively. Results show the strong and entangled relation among the wavelength, the domain geometries, and the artifact visualization, allowing for better understanding of propagation in such a complex medium and opening several possibilities for future studies.

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. 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.

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. This is reflected by changes in the appearance of the lung ultrasound images. In abnormal lungs, vertical artifacts known as B-lines appear and can evolve into white lung patterns in the more severe cases. Currently, these artifacts are assessed by trained physicians, and the diagnosis is qualitative and operator dependent. In this article, an automatic segmentation method using a convolutional neural network is proposed to automatically stage the progression of the disease. 1863 B-mode images from 203 videos obtained from 14 asymptomatic individual,14 confirmed COVID-19 cases, and 4 suspected COVID-19 cases were used. Signs of lung damage, such as the presence and extent of B-lines and white lung areas, are manually segmented and scored from zero to three (most severe). These manually scored images are considered as ground truth. Different test-training strategies are evaluated in this study. The results shed light on the efficient approaches and common challenges associated with automatic segmentation methods.

The diagnosis of pneumonia in a pregnant woman with coronavirus disease 2019 using maternal lung ultrasound.

Lung ultrasound examination has been demonstrated to be an accurate imaging method to detect pulmonary and pleural conditions. During pregnancy, there is a need for rapid assessment of the maternal lung in patients with suspected coronavirus disease 2019. We report our experience on lung ultrasound examination in the diagnosis of coronavirus disease 2019 pneumonia in a pregnant woman. Typical ultrasound features of this pulmonary pathology, including diffuse hyperechoic vertical artifacts with thickened pleural line and "white lung" with patchy distribution, were observed. We suggest point-of-care lung ultrasound examination as a diagnostic imaging tool in pregnant women with suspected coronavirus disease 2019.

Contrast-Enhanced Ultrasound in Patients With COVID-19: Pneumonia, Acute Respiratory Distress Syndrome, or Something Else?

Coronavirus disease 2019 (COVID-19) represents a very heterogeneous disease. Some aspects of COVID-19 pneumonia question the real nature of ground glass opacities and its consolidative lesions. It has been hypothesized that COVID-19 lung involvement could represent not only a viral effect but also an immune response induced by the infection, causing epithelial/endothelial lesions and coagulation disorders. We report 3 cases of COVID-19 pneumonia in which contrast-enhanced ultrasound was suggestive of consolidations with perfusion defects, at least in part caused by ischemic or necrotic changes and not only by inflammatory or atelectasis events.

Proposal for International Standardization of the Use of Lung Ultrasound for Patients With COVID-19: A Simple, Quantitative, Reproducible Method.

Growing evidence is showing the usefulness of lung ultrasound in patients with the 2019 new coronavirus disease (COVID-19). Severe acute respiratory syndrome coronavirus 2 has now spread in almost every country in the world. In this study, we share our experience and propose a standardized approach to optimize the use of lung ultrasound in patients with COVID-19. We focus on equipment, procedure, classification, and data sharing.

Lung ultrasound: The future ahead and the lessons learned from COVID-19.

Lung ultrasound (LUS) is a rapidly evolving field of application for ultrasound technologies. Especially during the current pandemic, many clinicians around the world have employed LUS to assess the condition of the lung for patients suspected and/or affected by COVID-19. However, LUS is currently performed with standard ultrasound imaging, which is not designed to cope with the high air content present in lung tissues. Nowadays LUS lacks standardization and suffers from the absence of quantitative approaches. To elevate LUS to the level of other ultrasound imaging applications, several aspects deserve attention from the technical and clinical world. This overview piece tries to provide the reader with a forward-looking view on the future for LUS.

On the influence of imaging parameters on lung ultrasound B-line artifacts, in vitro study.

The clinical relevance of lung ultrasonography (LUS) has been rapidly growing since the 1990s. However, LUS is mainly based on the evaluation of visual artifacts (also called B-lines), leading to subjective and qualitative diagnoses. The formation of B-lines remains unknown and, hence, researchers need to study their origin to allow clinicians to quantitatively evaluate the state of lungs. This paper investigates an ambiguity about the formation of B-lines, leading to the formulation of two main hypotheses. The first hypothesis states that the visualization of these artifacts is linked only to the dimension of the emitted beam, whereas the second associates their appearance to specific resonance phenomena. To verify these hypotheses, the frequency spectrum of B-lines was studied by using dedicated lung-phantoms. A research programmable platform connected to an LA533 linear array probe was exploited both to implement a multifrequency approach and to acquire raw radio frequency data. The strength of each artifact was measured as a function of frequency, focal point, and transmitting aperture by means of the artifact total intensity. The results show that the main parameter that influences the visualization of B-lines is the frequency rather than the focal point or the number of transmitting elements.

Real-time multi-frequency ultrasound imaging for quantitative lung ultrasound - first clinical results.

Lung ultrasound imaging is a fast-evolving field of application for ultrasound technologies. However, most diagnoses are currently performed with imaging protocols that assume a quasi-homogeneous speed of sound in the volume of interest. When applied to the lung, due to the presence of air, this assumption is unrealistic. Consequently, diagnoses are often based on imaging artifacts and thus qualitative and subjective. In this paper, we present an image formation protocol that is capable of capturing the frequency dependence of well-known artifacts (B-lines) and visualizing it in real time, ultimately providing a quantitative assessment of the signals received from the lung. Previous in vitro studies have shown the potential of B-lines native-frequency for the characterization of bubbly medium, but this paper presents the first results on clinical data. The image formation process has been designed to work on lung tissue, and ultrasound images generated with four orthogonal bands centered at 3, 4, 5 and 6 MHz can be acquired and displayed in real time. Results show that B-lines can be characterized on the basis of their native frequency in vivo and open the way toward real-time quantitative lung ultrasound imaging.