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US Geological Survey (USGS) National Seismic Hazard Models (NSHMs) are used extensively for seismic design regulations in the United States and earthquake scenario development, as well as risk assessment and mitigation for both buildings and infrastructure. This 2023 update of the long-term, time-independent Alaska NSHM includes substantial changes to both the earthquake rupture forecast (ERF) and ground motion models (GMMs). The ERF includes numerous additions to the finite-fault model, considers two deformation models, and introduces updated declustering and smoothing algorithms in the gridded background seismicity model. For the Alaska–Aleutian subduction zone, megathrust earthquakes occur on an updated structural and segmentation model, and the moment magnitude (M) 8+ rupture and rate model include a logic tree branch that considers slip rates derived from geodetic models of interface coupling. The megathrust model considers multiple models of down-dip width, and magnitudes are computed using newly developed scaling relations. For subduction intraslab events and subduction interface events with M < 7, the 2023 update uses a smoothed seismicity model with rupture depths derived from Slab2. The 2023 model updates GMMs in all tectonic settings using the recently published Next Generation Attenuation Subduction (NGA-Sub) GMMs for subduction interface and intraslab events, and the NGA-West2 GMMs for active crustal settings. Collectively, additions and updates to the Alaska NSHM result in hazard increases across most of south-central Alaska relative to the previous model, published in 2007. These changes are primarily due to the adoption of updated rate models for the large-magnitude interface events and the NGA-Sub GMMs that have much higher aleatory variability (sigma), consistent with global observations, and that include models of epistemic uncertainty. [ABSTRACT FROM AUTHOR]

Neonatal respiratory disorders pose significant challenges in clinical settings, often requiring rapid and accurate diagnostic solutions for effective management. Lung ultrasound (LUS) has emerged as a promising tool to evaluate respiratory conditions in neonates. This evaluation is mainly based on the interpretation of visual patterns (horizontal artifacts, vertical artifacts, and consolidations). Automated interpretation of these patterns can assist clinicians in their evaluations. However, developing AI-based solutions for this purpose is challenging, primarily due to the lack of annotated data and inherent subjectivity in expert interpretations. This study aims to propose an automated solution for the reliable interpretation of patterns in LUS videos of newborns. We employed two distinct strategies. The first strategy is a frame-to-video-level approach that computes frame-level predictions from deep learning (DL) models trained from scratch (F2V-TS) along with fine-tuning pre-trained models (F2V-FT) followed by aggregation of those predictions for video-level evaluation. The second strategy is a direct video classification approach (DV) for evaluating LUS data. To evaluate our methods, we used LUS data from 34 neonatal patients comprising of 70 exams with annotations provided by three expert human operators (3HOs). Results show that within the frame-to-video-level approach, F2V-FT achieved the best performance with an accuracy of 77% showing moderate agreement with the 3HOs. while the direct video classification approach resulted in an accuracy of 72%, showing substantial agreement with the 3HOs, our proposed study lays down the foundation for reliable AI-based solutions for newborn LUS data evaluation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: Mitochondrial dysfunction is one of the earliest pathological events observed in amyotrophic lateral sclerosis (ALS). The aim of this study is to evaluate the therapeutic efficacy of 2,4-dinitrophenol (DNP), a mild mitochondrial uncoupler, in an ALS mouse model to provide preclinical proof-of-concept evidence of using DNP as a potential therapeutic drug for ALS., Methods: hSOD1 G93A mice were treated with 0.5-1.0 mg/kg DNP through daily oral gavage from presymptomatic stage or disease onset until 18 weeks old. Longitudinal behavioral studies were performed weekly or biweekly from 6 to 18 weeks old. In situ muscle contraction measurements in extensor digitorum longus muscles were conducted to evaluate the preservation of contractile force and motor unit numbers in hSOD1 G93A mice following DNP treatment. Muscle innervation and inflammatory markers were assessed using immunostaining. Extent of protein oxidation and activation of Akt pathway were also examined., Results: DNP delayed disease onset; improved motor coordination and muscle performance in vivo; preserved muscle contractile function, neuromuscular junction morphology, and muscle innervation; and reduced inflammation and protein oxidation at 18 weeks old in hSOD1 G93A mice. Strikingly, symptomatic hSOD1 G93A mice exhibited a period of recovery in running ability at 20 cm/s several weeks after 2,4-dinitrophenol treatment started at disease onset, offering the first observation in disease phenotype reversal using a small molecule., Interpretation: Our results strongly support that micro-dose DNP may be used as a potential novel treatment for ALS patients, with a possibility for recovery, when used at optimal doses and time of intervention. ANN NEUROL 2024., (© 2024 The Author(s). Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Background & Aims: There is an unmet need for a reliable and reproducible non-invasive measure of fatty liver content (FLC) for monitoring steatotic liver disease in clinical practice. Sonographic FLC assessment is qualitative and operator-dependent, and the dynamic quantification range of algorithms based on a single ultrasound (US) parameter is unsatisfactory. This study aims to develop and validate a new multiparametric algorithm based on B-mode images to quantify FLC using Magnetic Resonance (MR) values as standard reference., Methods: Patients with elevated liver enzymes and/or bright liver at US (N = 195) underwent FLC evaluation by MR and by US. Five US-derived quantitative features [attenuation rate(AR), hepatic renal-ratio(HR), diaphragm visualization(DV), hepatic-portal-vein-ratio(HPV), portal-vein-wall(PVW)] were combined by mixed linear/exponential regression in a multiparametric model (Steatoscore2.0). One hundred and thirty-four subjects were used for training and 61 for independent validations; score-computation underwent an inter-operator reproducibility analysis., Results: The model is based on a mixed linear/exponential combination of 3 US parameters (AR, HR, DV), modelled by 2 equations according to AR values. The computation of FLC by Steatoscore2.0 (mean ± std, 7.91% ± 8.69) and MR (mean ± std, 8.10% ± 10.31) is highly correlated with a low root mean square error in both training/validation cohorts, respectively (R = 0.92/0.86 and RMSE = 5.15/4.62, p < .001). Steatoscore2.0 identified patients with MR-FLC≥5%/≥10% with sensitivity = 93.2%/89.4%, specificity = 86.1%/95.8%, AUROC = 0.958/0.975, respectively and correlated with MR (R = 0.92) significantly (p < .001) better than CAP (R = 0.73)., Conclusions: Multiparametric Steatoscore2.0 measures FLC providing values highly comparable with MR. It is reliable, inexpensive, easy to use with any US equipment and qualifies to be tested in larger, prospective studies as new tool for the non-invasive screening and monitoring of FLC., (© 2024 The Author(s). Liver International published by John Wiley & Sons Ltd.)

Lung ultrasound (LUS) is an important imaging modality to assess the state of the lung surface. However, current LUS approaches are based on subjective interpretation of imaging artifacts, which results in poor specificity as quantitative evaluation lacks. The latter could be improved by adopting LUS spectroscopy of vertical artifacts. Indeed, parameterizing these artifacts with native frequency, bandwidth, and total intensity ( [Formula: see text]) already showed potentials in differentiating pulmonary fibrosis (PF). In this study, we acquired radio frequency (RF) data from 114 patients. These data (representing the largest LUS RF dataset worldwide) were acquired by utilizing a multifrequency approach, implemented with an ULtrasound Advanced Open Platform (ULA-OP). Convex (CA631) and linear (LA533) probes (Esaote, Florence, Italy) were utilized to acquire RF data at three (2, 3, and 4 MHz), and four (3, 4, 5, and 6 MHz) imaging frequencies. A multifrequency analysis was conducted on vertical artifacts detected in patients having cardiogenic pulmonary edema (CPE), pneumonia, or PF. These artifacts were characterized by the three abovementioned parameters, and their mean values were used to project each patient into a feature space having up to three dimensions. Binary classifiers were used to evaluate the performance of these three mean features in differentiating patients affected by CPE, pneumonia, and PF. Acquisitions of multifrequency data performed with linear probe lead to accuracies up to 85.43% in the differential diagnosis of these diseases (convex probes' maximum accuracy was 74.51%). Moreover, the results showed high potentials of mean [Formula: see text] (by itself or combined with other features) in improving LUS specificity.

Pneumonia is the leading cause of death among children around the world. According to WHO, a total of 740,180 lives under the age of five were lost due to pneumonia in 2019. Lung ultrasound (LUS) has been shown to be particularly useful for supporting the diagnosis of pneumonia in children and reducing mortality in resource-limited settings. The wide application of point-of-care ultrasound at the bedside is limited mainly due to a lack of training for data acquisition and interpretation. Artificial Intelligence can serve as a potential tool to automate and improve the LUS data interpretation process, which mainly involves analysis of hyper-echoic horizontal and vertical artifacts, and hypo-echoic small to large consolidations. This paper presents, Fused Lung Ultrasound Encoding-based Transformer (FLUEnT), a novel pediatric LUS video scoring framework for detecting lung consolidations using fused LUS encodings. Frame-level embeddings from a variational autoencoder, features from a spatially attentive ResNet-18, and encoded patient information as metadata combiningly form the fused encodings. These encodings are then passed on to the transformer for binary classification of the presence or absence of consolidations in the video. The video-level analysis using fused encodings resulted in a mean balanced accuracy of 89.3 %, giving an average improvement of 4.7 % points in comparison to when using these encodings individually. In conclusion, outperforming the state-of-the-art models by an average margin of 8 % points, our proposed FLUEnT framework serves as a benchmark for detecting lung consolidations in LUS videos from pediatric pneumonia patients., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Libertario Demi is the co-founder of UltraAI. The rest of authors declare no conflict of interests., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: Static 3-dimensional (3D) printing became attractive for operative planning in cases that involve difficult anatomy. An interactive (low cost, fast) 3D print allowing deliberate surgical practice can be used to improve interventional simulation and planning., Background: Endovascular treatment of complex aortic aneurysms is technically challenging, especially in case of narrow aortic lumen or significant aortic angulation (hostile anatomy). The risk of complications such as graft kinking and target vessel occlusion is difficult to assess based solely on traditional software measuring methods and remain highly dependent on surgeon skills and expertise., Methods: A patient with juxtarenal AAA with hostile anatomy had a 3-dimensional printed model constructed preoperatively according to computed tomography images. Endovascular graft implantation in the 3D printed aorta with a standard T-Branch Cook (Cook® Medical, Bloomington, IN, USA) was performed preoperatively in the simulation laboratory enabling optimized feasibility, surgical planning and intraoperative decision making., Results: The 3D printed aortic model proved to be radio-opaque and allowed simulation of branched endovascular aortic repair (BREVAR). The assessment of intervention feasibility, as well as optimal branch position and orientation was found to be useful for surgeon confidence and the actual intervention in the patient. There was a remarkable agreement between the 3D printed model and both CT and X-ray angiographic images. Although the technical success was achieved as planned, a previously deployed renal stent caused unexpected difficulty in advancing the renal stent, which was not observed in the 3D model simulation., Conclusion: The 3D printed aortic models can be useful for determining feasibility, optimizing planning and intraoperative decision making in hostile anatomy improving the outcome. Despite already offering satisfying accuracy at present, further advancements could enhance the 3D model capability to replicate minor anatomical deformities and variations in tissue density., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ultrasound localization microscopy (ULM) allows for the generation of super-resolved (SR) images of the vasculature by precisely localizing intravenously injected microbubbles. Although SR images may be useful for diagnosing and treating patients, their use in the clinical context is limited by the need for prolonged acquisition times and high frame rates. The primary goal of our study is to relax the requirement of high frame rates to obtain SR images. To this end, we propose a new time-efficient ULM (TEULM) pipeline built on a cutting-edge interpolation method. More specifically, we suggest employing Radial Basis Functions (RBFs) as interpolators to estimate the missing values in the 2-dimensional (2D) spatio-temporal structures. To evaluate this strategy, we first mimic the data acquisition at a reduced frame rate by applying a down-sampling (DS = 2, 4, 8, and 10) factor to high frame rate ULM data. Then, we up-sample the data to the original frame rate using the suggested interpolation to reconstruct the missing frames. Finally, using both the original high frame rate data and the interpolated one, we reconstruct SR images using the ULM framework steps. We evaluate the proposed TEULM using four in vivo datasets, a Rat brain (dataset A), a Rat kidney (dataset B), a Rat tumor (dataset C) and a Rat brain bolus (dataset D), interpolating at the in-phase and quadrature (IQ) level. Results demonstrate the effectiveness of TEULM in recovering vascular structures, even at a DS rate of 10 (corresponding to a frame rate of sub-100Hz). In conclusion, the proposed technique is successful in reconstructing accurate SR images while requiring frame rates of one order of magnitude lower than standard ULM.

Automated cardiac segmentation from two-dimensional (2D) echocardiographic images is a crucial step toward improving clinical diagnosis. Anatomical heterogeneity and inherent noise, however, present technical challenges and lower segmentation accuracy. The objective of this study is to propose a method for the automatic segmentation of the ventricular endocardium, the myocardium, and the left atrium, in order to accurately determine clinical indices. Specifically, we suggest using the recently introduced pixel-to-pixel Generative Adversarial Network (Pix2Pix GAN) model for accurate segmentation. To accomplish this, we integrate the backbone PatchGAN model for the discriminator and the UNET for the generator, for building the Pix2Pix GAN. The resulting model produces precisely segmented images, thanks to UNET's capability for precise segmentation and PatchGAN's capability for fine-grained discrimination. For the experimental validation, we use the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) dataset, which consists of echocardiographic images from 500 patients in 2-chamber (2CH) and 4-chamber (4CH) views at the end-diastolic (ED) and end-systolic (ES) phases. Similarly to state-of-the-art studies on the same dataset, we followed the same train-test splits. Our results demonstrate that the proposed GAN-based technique improves segmentation performance for clinical and geometrical parameters compared to the state-of-the-art methods. More precisely, throughout the ED and ES phases, the mean Dice values for the left ventricular endocardium reached 0.961 and 0.930 for 2CH, and 0.959 and 0.950 for 4CH, respectively. Furthermore, the average ejection fraction correlation and Mean Absolute Error obtained were 0.95 and 3.2ml for 2CH, and 0.98 and 2.1ml for 4CH, outperforming the state-of-the-art results.

See also the commentary by Sitek in this issue. Supplemental material is available for this article.

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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Libertario Demi reports a relationship with Esaote that includes: funding grants. The rest of authors declare no conflict of interests., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Lung ultrasound (LUS) is an important imaging modality to assess the state of the lung surface. Nevertheless, LUS is limited to the visual evaluation of imaging artifacts, especially the vertical ones. These artifacts are observed in pathologies characterized by a reduction of dimensions of air-spaces (alveoli). In contrast, there exist pathologies, such as chronic obstructive pulmonary disease (COPD), in which an enlargement of air-spaces can occur, which causes the lung surface to behave essentially as a perfect reflector, thus not allowing ultrasound penetration. This characteristic high reflectivity could be exploited to characterize the lung surface. Specifically, air-spaces of different sizes could cause the lung surface to have a different roughness, whose estimation could provide a way to assess the state of the lung surface. In this study, we present a quantitative multifrequency approach aiming at estimating the lung surface's roughness by measuring image intensity variations along the lung surface as a function of frequency. This approach was tested both in silico and in vitro, and it showed promising results. For the in vitro experiments, radiofrequency (RF) data were acquired from a novel experimental model. The results showed consistency between in silico and in vitro experiments., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

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., (© 2023 Acoustical Society of America.)

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., Competing Interests: Prof. Demi is cofounder of UltraAI (Trento, IT); received funding from the European Institute for Innovation and Technology, Fondazione Valorizzazione Ricerca Trentina, the National Research Council (CNR), and Esaote(IT).Dr Wolfram receives Funding from the Federal Ministry of Education and Research (BMBF, Germany)., (Thieme. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Increasing temporal resolution through numerical methods aids clinicians to evaluate fast moving structures of the heart with more confidence., Methodology: In this study, a spatio-temporal numerical method is proposed to increase the frame rate based on two-dimensional (2D) interpolation. More specifically, we propose a novel intensity variation time surface (IVTS) strategy to incorporate both temporal and spatial information in the reconstruction. In this regard, we exploit radial basis functions (RBFs) for 2D interpolation. The reason for choosing RBFs for this task is manifold. First, RBFs are able to interpolate on large-scale datasets. Moreover, their mathematical implementation is simple. Another important property of this interpolation technique, which is addressed in this study, is its meshless nature. The meshless property enables higher up-sampling (UpS) rates for echocardiography to improve temporal resolution without noticeably degrading image quality. To evaluate the proposed approach, we tested the RBF interpolation on 2D/3D echocardiography datasets. The reconstructed frames were analyzed using different image quality metrics, and the results were compared with two popular techniques from the literature., Results: The findings demonstrated that, with a down-sampling rate of 3, the proposed technique outperformed the best existing method by 42%, 87%, 8%, and 11%, respectively, in terms of mean square error (MSE), contrast to noise ratio (CNR), peak signal-to-noise ratio (PSNR), and figure of merit (FOM). It should be noted that the proposed method is comparable to the best available method in terms of structural similarity (SSIM) index. Furthermore, when compared to the original images, the results of employing our technique on radio-frequency (RF) level analysis demonstrated that the reconstruction accuracy is satisfactory in terms of image quality criterion., Conclusion: Finally, it is worthwhile noting that the proposed method is better than (or comparable to) the other methods in terms of reconstruction performance and processing time. Therefore, the RBF interpolation can be a promising alternative to the existing methods., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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., (© 2022 The Authors. Journal of Ultrasound in Medicine published by Wiley Periodicals LLC on behalf of American Institute of Ultrasound in Medicine.)

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., (© 2022 The Authors. Journal of Ultrasound in Medicine published by Wiley Periodicals LLC on behalf of American Institute of Ultrasound in Medicine.)

High frame rate ultrasound (US) imaging enables the monitoring of fast-moving organs. In echocardiography, this is especially needed due to the existence of rapidly moving structures, such as the heart valves. In the last two decades, various methods have been proposed to improve the frame rate. Here, we propose a novel method, based on binary coding patterns (BCPs) and tensor completion (TC), to increase the temporal resolution (i.e., frame rate) in the preprocessing stage of conventional focused ultrasound imaging (CFUI). The rationale behind our proposal is to perform, at first, the beamforming of a fraction of the scan lines, randomly selected in each frame based on BCP. Then, we reconstruct the missing scan lines through TC. The latter is an effective technique for recovering missing information from a low-rank tensor, based on a small number of observations using rank minimization. Following our approach, reducing the transmissions events needed to generate an image, the frame rate is increased by the same proportion. We have applied the proposed technique to a pre-beamformed radio frequency (RF) echocardiographic dataset. Our results show that we can improve the frame rate by a factor from 3 to 4, while keeping the structural similarity (SSIM) of the reconstructed tensor and the original one at values higher than 0.98.

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., Competing Interests: Libertario Demi is a cofounder of UltraAI. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest All authors declare no conflicts of interest., (Copyright © 2022 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

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., (© 2021 The Authors. Journal of Ultrasound in Medicine published by Wiley Periodicals LLC on behalf of American Institute of Ultrasound in Medicine.)

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. Various deep learning (DL) algorithms supported with clinical validations have been proposed to automate the stratification process. However, no work has been done to evaluate the impact on the automated decision by varying pixel resolution and bit depth, leading to the reduction in size of overall data. This article evaluates the performance of DL algorithm over LUS data with varying pixel and gray-level resolution. The algorithm is evaluated over a dataset of 448 LUS videos captured from 34 examinations of 20 patients. All videos are resampled by a factor of 2, 3, and 4 of original resolution, and quantized to 128, 64, and 32 levels, followed by score prediction. The results indicate that the automated scoring shows negligible variation in accuracy when it comes to the quantization of intensity levels only. Combined effect of intensity quantization with spatial down-sampling resulted in a prognostic agreement ranging from 73.5% to 82.3%.These results also suggest that such level of prognostic agreement can be achieved over evaluation of data reduced to 32 times of its original size. Thus, laying foundation to efficient processing of data in resource constrained environments.

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.

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.

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.

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.