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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.

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.

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.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.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.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.

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.

Aims Chest ultrasound is a non-invasive method for evaluating children with suspected community-acquired pneumonia (CAP). We evaluated the prognostic role of change of ultrasonographic (US) air bronchogram in management of CAP in terms of: rate of complicated CAP, change of empiric antibiotic therapy, relationship to defervescence time, and length of hospitalization. Methods Patients with CAP and radiographic evidence of lung consolidation were prospectively enrolled. Chest US examinations were performed within 12 h from admission and after 48 h. A new grading system (USINCHILD score) based on presence and features of air bronchogram was adopted. Results Thirty six patients were stratified into two groups according to the presence of an increase of at least 1 grade of US score (Δ US grade), expression of an improvement of lung consolidation. Δ US grade after 48 h ≥ 1 was associated with an increased risk of complicated CAP (p value 0.027) and a longer defervescence time (p value 0.036). Moreover, Δ US grade ≥ 1 was predictive of a short hospitalization (p value 0.008). Conclusions USINCHILD score could be an innovative biotechnology tool for the management of pediatric CAP. Trial registration number and date of registration NCT03556488, June 14, 2018. Graphic abstract

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 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.

COVID-19 pandemic is representing a serious challenge to worldwide public health. Lung Ultrasonography (LUS) has been signaled as a potential useful tool in this pandemic contest either to intercept viral pneumonia or to foster alternative paths. LUS could be useful in determining early lung involvement suggestive or not of COVID-19 pneumonia and potentially plays a role in managing decisions for hospitalization in isolation or admission in general ward. In order to face pandemic, in a period in which a large number of emergency room accesses with suspicious symptoms are expected, physicians need a standardized ultrasonographic approach, fast educational processes in order to be able to recognize both suggestive and not suggestive echographic signs and shared algorithms for LUS role in early management of patients.

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.

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 COVID-19. Copyright © 2020 ISUOG. Published by John Wiley & Sons Ltd.

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.

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.

In the last years, efforts have been made towards automating semi-quantitative analysis of lung ultrasound (LUS) data. To this end, several methods have been proposed with a focus on frame-level classification. However, no extensive work has been done to evaluate LUS data directly at the video level. This study proposes an effective video compression and classification technique for assessing LUS data. This technique is based on maximum, mean, and minimum intensity projection (with respect to the temporal dimension) of LUS video data. This compression allows preserving hyper- and hypo-echoic regions and results in compressing a LUS video down to three frames, which are then classified using a convolutional neural network (CNN). Results show that this compression not only preserves visual artifacts appearance in the reduced data, but also achieves a promising agreement of 81.61% at the prognostic level. Conclusively, the suggested method reduces the amount of frames needed to assess LUS video down to 3. Note that on average a LUS videos consists of a few hundreds frames. At the same time, state-of-the-art performance at video and prognostic levels are achieved, while significantly reducing the computational cost.

The goal of this study is to test the applicability of a generative adversarial network (GAN) to solve the class unbalancing problem in lung ultrasound (LUS) data. We introduce a supervised autoencoder with conditional latent space. During training, the generator utilizes the weights of the decoder and conditional latent space to generate synthetic LUS images, whereas the discriminator utilizes the weights of the encoder together with the class labels, which also allows classifying each synthetic image into the different classes. A customized gradient penalty loss function was utilised. This approach is tested on a dataset costing of 6500 LUS images, collected from 35 COVID-19 patients and labelled according to a validated 4 level scoring system [10.1109/TMI.2020.2994459]. 1000 synthetic images were generated to balance this dataset. The quality of the synthetic images was evaluated through similarity measures, computed with respect to training and unseen data. Moreover, the synthetic images were also evaluated by expert clinicians concerting their capability to mimic a realistic LUS image. In conclusion, the proposed approach appears to be capable to solve the class unbalancing problem by generating LUS images carrying novel information content, comparable to that of real data from new patients.

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.

In the current pandemic, being able to efficiently stratify patients depending on their probability to develop a severe form of COVID-19 can improve the outcome of treatments and optimize the use of the available resources. To this end, recent studies proposed to use deep-networks to perform automatic stratification of COVID-19 patients based on lung ultrasound (LUS) data. In this work, we present a novel neuro-symbolic approach able to provide video-level predictions by aggregating results from frame-level analysis made by deep-networks. Specifically, a decision tree was trained, which provides direct access to the decision process and a high-level explainability. This approach was tested on 1808 LUS videos acquired from 100 patients diagnosed as COVID-19 positive by a RT-PCR swab test. Each video was scored by LUS experts according to a 4-level scoring system specifically developed for COVID-19. This information was utilised for both the training and testing of the algorithms. A five-folds cross-validation process was utilised to assess the performance of the presented approach and compare it with results achieved by deep-learning models alone. Results show that this novel approach achieves better performance (82% of mean prognostic agreement) than a threshold-based ensemble of deep-learning models (78% of mean prognostic agreement).

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 (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) imaging is playing an important role in the current pandemic, allowing the evaluation of patients affected by COVID-19 pneumonia. However, LUS is limited to the visual inspection of ultrasound data, which negatively affects the reproducibility and reliability of the findings. For these reasons, we were the first to propose a standardized imaging protocol and a scoring system, from which we developed the first artificial intelligence (AI) models able to evaluate LUS videos. Furthermore, we demonstrated the prognostic value of our approach and its utility for patients' stratification. In this study, we report on the level of agreement between the AI and LUS clinical experts (MD) when evaluating LUS data. Specifically, in the stratification between patients at high risk of clinical worsening and patients at low risk, the agreement between MDs and AI reached 82%. These encouraging results open to the possibility of exploiting AI for fast and accurate stratification of COVID-19 patients.

Background: Lung ultrasound (LUS) has been reported as a useful tool to intercept lung peripheral changes (LPC) in COVID-19 pneumonia. Viral pneumonia by Sars-Cov-2 is characterized by alveolar exudation and damage, interstitial thickening with patchy distribution with centrifugal extension and subpleural involvement. Methods: COVID-19 pneumonia patients underwent LUS using a standard sequence of scans in 14 landmarks. A score ranging from 0 to 3, according to Soldati’s proposal, was reported for each landmark. High-resolution CT-scan of the chest (HRCT) was performed within 48 hours prior to or after LUS. For each corresponding HRCT area, was reported a score (0 normal peripheral lung, 1 minimal LPC, 2 peripheral ground glass opacities (GGOs), 3 peripheral lung consolidations with or without GGOs) Results: Sixteen confirmed COVID-19 pneumonia underwent LUS. LUS showed sensitivity 92.1%, specificity 90%, PPV 96.8% to intercept any peripheral alterations on HRCT (both LUS and HRCT scores ≠0). Higher LUS scores (2-3), corresponding to worst changes, showed sensitivity 70.1%, specificity 84%, PPV 78.1% to intercept higher HRCT scores (2-3). The overall score, for both LUS and HRCT, over 14 landmarks, showed no significant differences (paired t-test p=0.055). An overall score ≥24 was reported in 5 cases by LUS and 6 cases by HRCT. No significant differences also for patients either with more than 3 landmarks with score 3 or with 8 landmarks out of 14 with score 2-3 (p=0.16). Conclusions: LUS showed good sensitivities and specificities compared to HRCT to intercept peripheral changes in COVID-19 pneumonia.

Introduction: Thoracic ultrasound is accurate in the diagnosis of a wide range of respiratory diseases. Yet the extent of its use is unknown. Through a national survey, we aimed to explore the clinical use of thoracic ultrasound and the barriers to the diffusion of the technique in Italy. Methods: Accademia di Ecografia Toracica (AdET) developed a self-administered survey which was sent by email to Italian pulmonologists via national scientific societies and networks. Results: Of the 2010 physicians invited, 514 completed the survey (26% response rate). According to 99% of responders, thoracic ultrasound had a relevant clinical role. Seventy-nine percent of the responders used thoracic ultrasound at least once a month. The main settings were: 53% pulmonology ward, 15% outpatient clinic, 15% interventional pulmonology room, 10% internal medicine ward, 4% respiratory intensive care units, and 9% other. Thoracic ultrasound was primarily used: (1) with both diagnostic and interventional aims (72%), (2) as diagnostic imaging (17%), and (3) as guidance for interventional procedures (11%). The main clinical applications were: (1) diagnosis and management of pleural effusion, (2) pneumothorax, (3) pneumonia, (4) cardiac failure, and (5) acute dyspnea. Twenty-one percent of the responders do not use thoracic ultrasound. The main reported bar­riers were: (1) availability of an ultrasound system (52%), (2) lack of protected time and training (22%), and (3) use of the technique by other specialists (15%). Conclusion: Thoracic ultrasound is widely used by Italian pulmonologists and considered a clinically relevant tool. The availability of dedicated ultrasound systems seems to be a major limit of the use of the technique.

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.

Early detection of COVID-19 is key in containing the pandemic. Disease detection and evaluation based on imaging is fast and cheap and therefore plays an important role in COVID-19 handling. COVID-19 is easier to detect in chest CT, however, it is expensive, non-portable, and difficult to dis-infect, making it unfit as a point-of-care (POC) modality. On the other hand, chest X-ray (CXR) and lung ultrasound (LUS) are widely used, yet, COVID-19 findings in these modalities are not always very clear. Here we train deep neural networks to significantly enhance the capability to detect, grade and monitor COVID-19 patients using CXRs and LUS. Collaborating with several hospitals in Israel we collect a large dataset of CXRs and use this dataset to train a neural network obtaining above 90% detection rate for COVID-19. In addition, in collaboration with ULTRa (Ultrasound Laboratory Trento, Italy) and hospitals in Italy we obtained POC ultrasound data with annotations of the severity of disease and trained a deep network for automatic severity grading.

During the COVID-19 pandemic, lung ultrasound (LUS) has been adopted by clinicians worldwide to study the effect of the virus on the lungs. However, LUS suffers from the qualitative and subjective nature of the adopted methodologies. To this end, we were the first to design, implement and validate a standardized imaging protocol (based on 14 acquisition points) and scoring system (on 4 levels). Moreover, we trained the first artificial intelligence (AI) models capable of assessing LUS videos providing, for each video-frame, the score as well as visual feedback in the form of semantic segmentation. The latter serves as explanation, is crucial to facilitate the clinician engagement in the evaluation, and guarantees a correct integration of AI into clinical practice. In this multicentre study, we report on the level of agreement between AI and LUS experts. 1,547 LUS videos from 82 patients (43 male and 39 female, mean age of 61, 23-95), corresponding to 325,568 frames, were analysed. All patients were COVID-19 positive as confirmed by RT-PCR test. Results show a perfect agreement between LUS experts and AI for 51.7% of the videos. This percentage increases to 87.5% when allowing a disagreement up to 1 point (over a score range from 0 to 3). Figure 1 shows the agreement at video-level (top) and at the exam level (bottom). The latter score is obtained summing the scores over the 14 acquisition points. These promising results illustrate the potentials of AI in supporting clinicians in the evaluation of LUS data.

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.

Amiodarone is a drug commonly used to treat and prevent cardiac arrhythmias, but it is often associated with several adverse effects, the most serious of which is pulmonary toxicity. A 79-year-old man presented with respiratory failure due to interstitial pneumonia during coronavirus disease 2019 (COVID-19) pandemic. The viral etiology was nevertheless excluded by repeated nasopharyngeal swabs and serological tests and the final diagnosis was amiodarone induced organizing pneumonia. The clinical and computed tomography findings improved after amiodarone interruption and steroid therapy. Even during a pandemic, differential diagnosis should always be considered and pulmonary toxicity has to be taken into account in any patient taking amiodarone and who has new respiratory symptoms., Lay abstract Amiodarone is a drug used to treat and prevent heart rhythm alterations (cardiac arrhythmias). Although it is effective, it is associated with some side effects, including pulmonary toxicity (in 2–18% of patients), which is a serious side effect impacting the lungs of the patient. A 79-year-old man presented with a dry cough and shortness of breath during the coronavirus disease 2019 (COVID-19) pandemic, but repeatedly tested negative for the virus. The diagnosis was determined to be amiodarone induced pneumonia, a condition that inflames the air sacs in the lungs. The patient stopped taking amiodarone and steroid therapy was started, leading to an improvement in the patient's condition. Even if the diagnosis initially seemed to be of COVID-19, amiodarone toxicity should always be considered in patients taking this drug and with respiratory symptoms.

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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.