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.

Avoidance of corticosteroids could be beneficial after pediatric liver transplantation (LTx). To test this hypothesis, we performed a randomized prospective study to compare immunosuppression with tacrolimus (TAC) and steroids versus TAC and basiliximab (BAS) after pediatric LTx. Seventy-two patients were recruited, 36 receiving TAC and steroids and 36 TAC and BAS. The primary endpoint was the occurrence of the first rejection episode. Secondary endpoints were the cumulative incidence and severity of rejection, patient and graft survival, and incidence of adverse events. Overall 1-year patient and graft survival rates were 91.4% and 85.5% in the steroid group, and 88.6% and 80% in the BAS group (p = NS). Patients free from rejection were 87.7% in the BAS group and 67.7% in the steroid group (p = 0.036). The use of BAS was associated with a 63.6% reduction in incidence of acute rejection episodes. Overall incidence of infection was 72.3% in the steroid group and 50% in the BAS group (p = 0.035). We conclude that the combination of TAC with BAS is an alternative to TAC and steroid immunosuppression in pediatric LTx, which allows for a significant reduction in the incidence of acute rejection and infectious complications.

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.