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.

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Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DLbased solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, videolevel, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.

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Lung ultrasound has been suggested recently by the Chinese Critical Care Ultrasound Study Group and Italian Academy of Thoracic Ultrasound as an accurate tool to detect lung involvement in COVID‐19. Although chest computed tomography (CT) represents the gold standard to assess lung involvement, with a specificity superior even to that of the nasopharyngeal swab for diagnosis, lung ultrasound examination can be a valid alternative to CT scan, with certain advantages, particularly for pregnant women. Ultrasound can be performed directly at the bed‐side by a single operator, reducing the risk of spreading the disease among health professionals. Furthermore, it is a radiation‐free exam, making it safer and easier to monitor those patients who require a series of exams. We report on four cases of pregnant women affected by COVID‐19 who were monitored with lung ultrasound examination. All patients showed sonographic features indicative of COVID‐19 pneumonia at admission: irregular pleural lines and vertical artifacts (B‐lines) were observed in all four cases, and patchy areas of white lung were observed in two. Lung ultrasound was more sensitive than was chest X‐ray in detecting COVID‐19. In three patients, we observed almost complete resolution of lung pathology on ultrasound within 96 h of admission. Two pregnancies were ongoing at the time of writing, and two had undergone Cesarean delivery with no fetal complications. Reverse transcription polymerase chain reaction analysis of cord blood and newborn swabs was negative in both of these cases. Copyright © 2020 ISUOG. Published by John Wiley & Sons Ltd.

ung sonography is widely accepted and used in emergency medicine and critical care. [1][2][3][4][5] Moreover, many pulmonologists are interested in chest sonography for the study of pleural diseases and are increasingly discovering a role for sonography in parenchymal lung diseases. [6][7][8][9] For those physicians who are devoted to chest sonography, a clear dichotomy between usual sonography and aerated tissue sonography is obvious. Pleural sonography is effective under most circumstances, whereas lung sonography is effective only when certain physical properties of the lung (eg, the bubble system) are lost. In other words, the lung is sonographically explorable only when it is physically comparable with soft tissue. In particular, when using lung sonography, a lung that contains dispersed air and has a density that is not comparable with the density of water does not show anatomic images but rather artifactual images. 10 Therefore, lung artifacts are quite consistent with the physical properties of a lung that is not fully consolidated rather than with an anatomic image. 11 The physical properties of the subpleural nonconsolidated lung are the hallmarks of many pulmonary diseases, which can be roughly grouped into "interstitial diseases." If an ultrasound imaging system is used, all of these pulmonary diseases are classified by the generic term "sonographic interstitial syndrome" (B-lines with variable arrangements along the pleural line). 5 According to this view, it is not surprising that since 1997, 12 vertical lung artifacts, commonly named B-lines, have been associated with pathologic conditions ranging from pulmonary edema to fibrosis, which are characterized by a change in the subpleural physical features in terms of full and empty spaces. 11

Under certain circumstances, such as during the current COVID-19 outbreak, pregnant women can be a target for respiratory infection, and lung examination may be required as part of their clinical evaluation, ideally while avoiding exposure to radiation. We propose a practical approach for obstetricians/gynecologists to perform lung ultrasound examination, discussing potential applications, semiology and practical aspects, which could be of particular importance in emergency situations, such as the current pandemic infection of

Recent works highlighted the significant potential of lung ultrasound (LUS) imaging in the management of subjects affected by COVID-19. In general, the development of objective, fast, and accurate automatic methods for LUS data evaluation is still at an early stage. This is particularly true for COVID-19 diagnostic. In this article, we propose an automatic and unsupervised method for the detection and localization of the pleural line in LUS data based on the hidden Markov model and Viterbi Algorithm. The pleural line localization step is followed by a supervised classification procedure based on the support vector machine (SVM). The classifier evaluates the healthiness level of a patient and, if present, the severity of the pathology, i.e., the score value for each image of a given LUS acquisition. The experiments performed on a variety of LUS data acquired in Italian hospitals with both linear and convex probes highlight the effectiveness of the proposed method. The average overall accuracy in detecting the pleura is 84% and 94% for convex and linear probes, respectively. The accuracy of the SVM classification in correctly evaluating the severity of COVID-19 related pleural line alterations is about 88% and 94% for convex and linear probes, respectively. The results as well as the visualization of the detected pleural line and the predicted score chart, provide a significant support to medical staff for further evaluating the patient condition.