Deep Learning (DL) has rapidly advanced Lung Ultrasound (LUS) image classification. Traditional DL uses Centralized Learning (CL) to train models, which requires large datasets for effective model training. While combining data across hospitals could provide sufficient samples, patient privacy may complicate direct data sharing. To solve this problem, Federated Learning (FL) can be employed. FL enables multiple clients to collaborate without exposing patient data, allowing each client to train models locally and share only weights for central aggregation. While previous works investigated the use of FL for LUS data analysis, the present study investigates, for the first time, whether FL maintains robustness across hospitals (compared with CL), specifically for the case of LUS data with diverse age distribution. Our experiments consider three configurations: (1) in Configuration 1, both FL and CL models are trained with adult LUS data from 4 hospitals; (2) in Configuration 2, a fifth client node with neonatal data is added; (3) in Configuration 3, the neonatal dataset is doubled. After training, all models are tested on two external test datasets. The results show that FL provides comparable and robust performance while keeping data private across demographically diverse institutions.

Over the last 20 years, scientific literature and interest on chest/lung ultrasound (LUS) have exponentially increased. Interpreting mixed-anatomical and artifactual-pictures determined the need of a proposal of a new nomenclature of artifacts and signs to simplify learning, spread, and implementation of this technique. The aim of this review is to collect and analyze different signs and artifacts reported in the history of chest ultrasound regarding normal lung, pleural pathologies, and lung consolidations. By reviewing the possible physical and anatomical interpretation of these artifacts and signs reported in the literature, this work aims to present the AdET (Accademia di Ecografia Toracica) proposal of nomenclature and to bring order between published studies.

Over the last 20 years, scientific literature and interest on chest/lung ultrasound (LUS) have exponentially increased. Interpreting mixed-anatomical and artifactual-pictures determined the need of a proposal of a new nomenclature of artifacts and signs to simplify learning, spread, and implementation of this technique. The aim of this review is to collect and analyze different signs and artifacts reported in the history of chest ultrasound regarding normal lung, pleural pathologies, and lung consolidations. By reviewing the possible physical and anatomical interpretation of these artifacts and signs reported in the literature, this work aims to present the AdET (Accademia di Ecografia Toracica) proposal of nomenclature and to bring order between published studies.

The exchange and flow of healthcare data are crucial and beneficial to develop automated methods for providing better care to patients. However, privacy is one of the major concerns when it comes to sharing medical data. Due to these concerns, limited data are available to develop such automated methods. To overcome this challenge, federated learning (FL), has gained significant interest within the research community as it allows to train deep learning (DL) models on different data sources without having to share data. In this study, our focus is on the evaluation of DL models trained to classify lung ultrasound (LUS) patterns in data acquired from multiple medical centers in FL setting. These patterns include horizontal artifacts, vertical artifacts, and small to large consolidations. To classify these patterns, we used ResNet-18 with spatial attention, trained and tested on 2104 LUS videos from 135 patients across 6 medical centers with a train-test split at the patient level. The classification performance of the patterns at frame and video levels is evaluated and the model’s capability to perform patients’ prognostic stratification is also assessed. We also compare it with an identical, centrally-trained model as the baseline method. Results show that the FL model achieved an overall accuracy of 57.5%, 48.6%, and 75% for frame, video, and prognostic levels, respectively, compared to 66.7%, 47.4%, and 80.8% achieved by the monolithic model. Despite a slight reduction in the classification performance compared to the monolithic model, FL demonstrates overall comparable results. These results underscore the efficacy of FL in enabling medical centers to learn collaboratively without the need to share raw data, thereby preserving privacy.

Lung ultrasound (LUS) is widely adopted to assess the state of the lung surface. However, as standard ultrasound imaging assumptions are unmet in the lung due to the presence of air, essentially this organ cannot be anatomically investigated. Indeed, LUS is mainly based on the analysis of imaging artifacts (horizontal and vertical). Of particular interest is the analysis of vertical artifacts, as they correlate with several pathologies. More specifically, their dependence on frequency was demonstrated to carry important diagnostic information capable to improve LUS specificity. In this study, a new dataset was generated acquiring raw radiofrequency (RF) data from 101 patients (affected by different pathologies, e.g., COVID-19 pneumonia and cardiogenic edema). The ULA-OP research platform was used to collect the data, and a multifrequency approach implemented on both linear and convex probes was adopted. Ultrasound data were thus acquired utilizing orthogonal sub-bands with a 1-MHz bandwidth and with different center frequencies (2–6 MHz). About 12 000 and 3600 multifrequency frames were acquired with convex and linear probes, respectively. Clinical parameters (e.g., FIO2 and LDH) were also stored for all patients. During this meeting, the dataset will be presented together with preliminary results, and its potential applications discussed.

Domain shift refers to change of data distribution between training and testing datasets. In case of medical imaging, domain shift is extensive, specifically for multi-center studies. Different medical centers may use different scanners, imaging protocols, subject populations, etc. To mitigate this effect, domain generalization (DG) has been used over the time. In this regard, our focus is to analyze if a pre-trained model can generalize lung ultrasound (LUS) pattern classification among pneumonia patients. Furthermore, if LUS data from one medical center is enough to generalize classification task across different medical centers. Investigated LUS patterns include horizontal artifacts, vertical artifacts, and small to large consolidations. As a proof of concept, data from medical center in Brescia (Italy) is used as source dataset whereas data from medical center in Rome (Italy) is considered as target dataset. ResNet-18 model (pre-trained on ImageNet dataset) is employed. The pre-trained model is trained on the source dataset with transfer learning using linear probing (LP) and linear probing with fine-tuning (LP-FT) approach. Results show that the pre-trained model can generalize much better over the target dataset when it undergoes LP-FT rather than only LP, achieving a mean F1-Score of 63.08%. These findings encourage the use of pre-trained models to generalize across different medical centers for LUS data analysis. Furthermore, they suggest that one medical center as the source dataset may be enough to generalize across other medical centers with state-of-the-art comparable performance for LUS pattern classification.

Background:Current available therapeutic options for Coronavirus Disease-2019 (COVID-19) are primarily focused on treating hospitalized patients, and there is a lack of oral therapeutic options to treat mild to moderate outpatient COVID-19 and prevent clinical progression. Raloxifene was found as a promising molecule to treat COVID-19 due to its activity to modulate the replication of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and act as an immunomodulator to decrease proinflammatory cytokines. Methods:This was a phase 2 multicenter, randomized, placebo-controlled trial to evaluate the efficacy and safety of raloxifene in adult patients with mild to moderate COVID-19 between October 2020 to June 2021 in five centers located in Italy. This was a planned 2/3 adaptive study, but due to operational difficulties, the study was discontinued during the phase 2 study segment. Participants were randomized 1:1:1 to receive oral placebo, raloxifene 60 mg, or raloxifene 120 mg by self-administration for a maximum of two weeks. The primary outcomes were the proportion of patients with undetectable SARS-CoV-2 via nasopharyngeal swabs at day 7 and the proportion of patients who did not require supplemental oxygen therapy or mechanical ventilation on day 14. Safety was assessed. The trial is registered (EudraCT 2021-002,476-39, and ClinicalTrials.gov: NCT05172050). Findings:A total of 68 participants were enrolled and randomized to placebo (n = 21), raloxifene 60 mg (n = 24), and raloxifene 120 mg (n = 23). The proportion of participants with undetectable SARS-CoV-2 after seven days of treatment with raloxifene 60 mg [36.8%, 7/19 vs. 0.0%, 0/14] and 120 mg [22.2%, 4/18 vs. 0.0%, 0/14] was better compared to placebo, [risk difference (RD) = 0·37 (95% C.I.:0·09-0·59)] and [RD = 0·22 (95% C.I.: -0·03-0·45)], respectively. There was no evidence of effect for requirement of supplemental oxygen and/or mechanical ventilation with effects for raloxifene 60 mg and raloxifene 120 mg over placebo, [RD = 0·09 (95% C.I.: -0·22-0·37)], and [RD = 0·03 (95% C.I.: -0·28-0·33)], respectively. Raloxifene was well tolerated at both doses, and there was no evidence of any difference in the occurrence of serious adverse events. Interpretation:Raloxifene showed evidence of effect in the primary virologic endpoint in the treatment of early mild to moderate COVID-19 patients shortening the time of viral shedding. The safety profile was consistent with that reported for other indications. Raloxifene may represent a promising pharmacological option to prevent or mitigate COVID-19 disease progression. Funding:The study was funded by Dompé Farmaceutici SpA and supported by the funds from the European Commission - Health and Consumers Directorate General, for the Action under the Emergency Support Instrument- Grant to support clinical testing of repurposed medicines to treat SARS-COV-2 patients (PPPA-ESI-CTRM-2020-SI2.837140), and by the COVID-2020-12,371,675 Ricerca finalizzata and line 1 Ricerca Corrente COVID both funded by Italian Ministry of Health.

In the last decades, an increasing interest about chest/lung ultrasound (LUS) made exponentially grow the scientific literature about it. In particular, the need of interpreting mixed pictures of anatomical and artifactual images has determined the proposal, from many authors, of simplified signs and artifacts nomenclature with the aim of making easier learning and implementation of the technique. Manuscript accepted for publication January 22, 2022. Address correspondence to Alessandro Zanforlin, MD, PhD, Servizio Pneumologico Aziendale, Piazza Loew-Cadonna 12, 39100 Bolzano, Italy.

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) examination has been shown to have a potential diagnostic and prognostic role in SARS-CoV-2 pneumonia disease. We evaluated the role of a new LUS score protocol (14 windows evaluation, graded score 0–3) in patients with SARS-CoV-2 pneumonia and the association of LUS patterns with clinical findings in acute stage and after three month from disease recovery. First, a cohort of 52 consecutive laboratory-confirmed SARS-CoV-2 patients underwent LUS examination upon the admission in an Internal Medicine ward. A total LUS score as the sum of the scores at each explored area was computed,and we investigated the association between LUS score and the clinical worsening. Then 47 patients who survived the first COVID-19 wave and who underwent a 3-stage LUS examination (T0 “access to ER”; T1 “ward hospitalization”; T2 “post-COVID outpatient”) were enrolled for the longitudinal study. In the acute stage, we observed that a median LUS score above 24 was associated with an almost 6-fold increase in the odds of worsening. In the longitudinal observation, we seen that LUS score's variation between T0 and T2 resulted to be statistically significant, as well a difference of LUS score between patients with or without pleural effusion, maintained over time.

Torri, Elena MD; Zanforlin, Alessandro MD, PhD; Soldati, Gino MD; Buonsenso, Danilo MD; Smargiassi, Andrea MD; Trobia, Gian Luca MD; Sferrazza Papa, Giuseppe Francesco MD; Mossolani, Elisa Eleonora MD; Inchingolo, Riccardo MD, PhD; Tursi, Francesco MD; Perrone, Tiziano MD, PhD Author Information

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.

Lung ultrasound (LUS) has been reported as a useful tool to intercept lung peripheral changes (LPC) in COVID-19 pneumonia. Sixteen confirmed 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 h 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) LUS showed sensitivity 92.1%, specificity 90%, PPV 96.8% to intercept LPC on HRCT (scores ≠ 0). Higher LUS scores (2–3), corresponding to worst changes, showed sensitivity 70.1%, specificity 84%, PPV 78.1% to intercept higher HTCT 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 five cases by LUS and 6 cases by HRCT. No significant differences also for patients either with more than three landmarks with score 3 or with 8 landmarks out of 14 with score 2–3 (p = 0.16). LUS showed good sensitivities and specificities compared to HRCT.

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.

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.

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.

Journal of Ultrasound in MedicineVolume 41, Issue 2 p. 525-526 Letter to the Editor LUS for COVID-19 Pneumonia: Flexible or Reproducible Approach? Gino Soldati MD, Gino Soldati MD Diagnostic and Interventional Ultrasound Unit, Valle del Serchio General Hospital, Lucca, ItalySearch for more papers by this authorAndrea Smargiassi MD, PhD, Corresponding Author Andrea Smargiassi MD, PhD [email protected] [email protected] orcid.org/0000-0003-0637-7282 Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy Address correspondence to Andrea Smargiassi, MD, PhD, Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Gemelli, 8, 00168 Rome, Italy. E-mail: [email protected], [email protected]Search for more papers by this authorTiziano Perrone MD, Tiziano Perrone MD orcid.org/0000-0001-8268-3121 Department of Internal Medicine and Therapeutics, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, ItalySearch for more papers by this authorElena Torri MD, Elena Torri MD Emergency Department, Humanitas Gavazzeni, Bergamo, ItalySearch for more papers by this authorFederico Mento MSc, Federico Mento MSc orcid.org/0000-0002-6571-7809 Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Trento, ItalySearch for more papers by this authorLibertario Demi PhD, Libertario Demi PhD orcid.org/0000-0002-0635-2133 Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Trento, ItalySearch for more papers by this authorRiccardo Inchingolo MD, PhD, Riccardo Inchingolo MD, PhD orcid.org/0000-0003-2843-9966 Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, ItalySearch for more papers by this author Gino Soldati MD, Gino Soldati MD Diagnostic and Interventional Ultrasound Unit, Valle del Serchio General Hospital, Lucca, ItalySearch for more papers by this authorAndrea Smargiassi MD, PhD, Corresponding Author Andrea Smargiassi MD, PhD [email protected] [email protected] orcid.org/0000-0003-0637-7282 Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy Address correspondence to Andrea Smargiassi, MD, PhD, Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Gemelli, 8, 00168 Rome, Italy. E-mail: [email protected], [email protected]Search for more papers by this authorTiziano Perrone MD, Tiziano Perrone MD orcid.org/0000-0001-8268-3121 Department of Internal Medicine and Therapeutics, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, ItalySearch for more papers by this authorElena Torri MD, Elena Torri MD Emergency Department, Humanitas Gavazzeni, Bergamo, ItalySearch for more papers by this authorFederico Mento MSc, Federico Mento MSc orcid.org/0000-0002-6571-7809 Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Trento, ItalySearch for more papers by this authorLibertario Demi PhD, Libertario Demi PhD orcid.org/0000-0002-0635-2133 Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Trento, ItalySearch for more papers by this authorRiccardo Inchingolo MD, PhD, Riccardo Inchingolo MD, PhD orcid.org/0000-0003-2843-9966 Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, ItalySearch for more papers by this author First published: 22 April 2021 https://doi.org/10.1002/jum.15726Citations: 2 All authors equally contributed to this work. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. References 1 Ma IWY, Noble VE, Mints G, et al. On recommending specific lung ultrasound protocols in the assessment of medical inpatients with known or suspected coronavirus Disease-19 reply. J Ultrasound Med 2021. https://doi.org/10.1002/jum.15650 [Epub ahead of print]. PubMedWeb of Science®Google Scholar 2Soldati G, Smargiassi A, Perrone T, et al. There is a validated acquisition protocol for lung ultrasonography in COVID-19 pneumonia. J Ultrasound Med 2021. https://doi.org/10.1002/jum.15649 [Epub ahead of print]. 10.1002/jum.15649 Web of Science®Google Scholar 3Ma IWY, Hussain A, Wagner M, et al. Canadian internal medicine ultrasound (CIMUS) expert consensus statement on the use of lung ultrasound for the assessment of medical inpatients with known or suspected coronavirus disease 2019. J Ultrasound Med 2020. https://doi.org/10.1002/jum.15571 [Epub ahead of print]. 10.1002/jum.15571 Web of Science®Google Scholar 4Soldati G, Smargiassi A, Inchingolo R, et al. Proposal for international standardization of the use of lung ultrasound for COVID-19 patients; a simple, quantitative, reproducible method. J Ultrasound Med 2020; 39: 1413–1419. 10.1002/jum.15285 PubMedWeb of Science®Google Scholar 5Soldati G, Smargiassi A, Mariani AA, Inchingolo R. Novel aspects in diagnostic approach to respiratory patients: is it the time for a new semiotics? Multidiscip Respir Med 2017; 27: 12–15. Google Scholar 6Perrone T, Soldati G, Padovini L, et al. A new lung ultrasound protocol able to predict worsening in patients affected by severe acute respiratory syndrome coronavirus 2 pneumonia. J Ultrasound Med 2020. https://doi.org/10.1002/jum.15548 [Epub ahead of print]. 10.1002/jum.15548 Web of Science®Google Scholar 7Mento F, Perrone T, Macioce VN, et al. On the impact of different lung ultrasound imaging protocols in the evaluation of patients affected by coronavirus disease 2019. J Ultrasound Med 2020. https://doi.org/10.1002/jum.15580 [Epub ahead of print]. 10.1002/jum.15580 Web of Science®Google Scholar 8Smargiassi A, Soldati G, Torri E, et al. Lung ultrasound for COVID-19 patchy pneumonia: extended or limited evaluations? J Ultrasound Med 2021; 40: 521–528. 10.1002/jum.15428 PubMedWeb of Science®Google Scholar 9Soldati G, Smargiassi A, Demi L, Inchingolo R. Artifactual lung ultrasonography: it is a matter of traps, order, and disorder. Appl. Sci 2020; 10:1570. https://doi.org/10.3390/app10051570. 10.3390/app10051570 CASWeb of Science®Google Scholar 10Soldati G, Demi M, Smargiassi A, Inchingolo R, Demi L. The role of ultrasound lung artifacts in the diagnosis of respiratory diseases. Expert Rev Respir Med 2019; 13: 163–172. 10.1080/17476348.2019.1565997 CASPubMedWeb of Science®Google Scholar 11Mento F, Soldati G, Prediletto R, Demi M, Demi L. Quantitative lung ultrasound spectroscopy applied to the diagnosis of pulmonary fibrosis: first clinical study. IEEE Trans Ultras Ferroelectr Freq Control 2020; 67: 2265–2273. 10.1109/TUFFC.2020.3012289 PubMedWeb of Science®Google Scholar Citing Literature Volume41, Issue2February 2022Pages 525-526 ReferencesRelatedInformation

Journal of Ultrasound in MedicineVolume 40, Issue 12 p. 2783-2783 Letter to the Editor There is a Validated Acquisition Protocol for Lung Ultrasonography in COVID-19 Pneumonia Gino Soldati MD, Gino Soldati MD Diagnostic and Interventional Ultrasound Unit, Valle del Serchio General Hospital, Lucca, ItalySearch for more papers by this authorAndrea Smargiassi MD, PhD, Corresponding Author Andrea Smargiassi MD, PhD [email protected] [email protected] orcid.org/0000-0003-0637-7282 Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Pulmonary Medicine Unit, Rome, ItalySearch for more papers by this authorTiziano Perrone MD, Tiziano Perrone MD orcid.org/0000-0001-8268-3121 Department of Internal Medicine and Therapeutics, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, ItalySearch for more papers by this authorElena Torri MD, Elena Torri MD Emergency Department, Humanitas Gavazzeni, Bergamo, ItalySearch for more papers by this authorFederico Mento MSc, Federico Mento MSc orcid.org/0000-0002-6571-7809 Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Trento, ItalySearch for more papers by this authorLibertario Demi PhD, Libertario Demi PhD orcid.org/0000-0002-0635-2133 Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Trento, ItalySearch for more papers by this authorRiccardo Inchingolo MD, PhD, Riccardo Inchingolo MD, PhD orcid.org/0000-0003-2843-9966 Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Pulmonary Medicine Unit, Rome, ItalySearch for more papers by this author Gino Soldati MD, Gino Soldati MD Diagnostic and Interventional Ultrasound Unit, Valle del Serchio General Hospital, Lucca, ItalySearch for more papers by this authorAndrea Smargiassi MD, PhD, Corresponding Author Andrea Smargiassi MD, PhD [email protected] [email protected] orcid.org/0000-0003-0637-7282 Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Pulmonary Medicine Unit, Rome, ItalySearch for more papers by this authorTiziano Perrone MD, Tiziano Perrone MD orcid.org/0000-0001-8268-3121 Department of Internal Medicine and Therapeutics, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, ItalySearch for more papers by this authorElena Torri MD, Elena Torri MD Emergency Department, Humanitas Gavazzeni, Bergamo, ItalySearch for more papers by this authorFederico Mento MSc, Federico Mento MSc orcid.org/0000-0002-6571-7809 Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Trento, ItalySearch for more papers by this authorLibertario Demi PhD, Libertario Demi PhD orcid.org/0000-0002-0635-2133 Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Trento, ItalySearch for more papers by this authorRiccardo Inchingolo MD, PhD, Riccardo Inchingolo MD, PhD orcid.org/0000-0003-2843-9966 Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Pulmonary Medicine Unit, Rome, ItalySearch for more papers by this author First published: 08 February 2021 https://doi.org/10.1002/jum.15649Citations: 3 All authors equally contributed to this study. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. References 1Ma IWY, Hussain A, Wagner M, et al. Canadian Internal Medicine Ultrasound (CIMUS) Expert Consensus Statement on the use of lung ultrasound for the assessment of medical inpatients with known or suspected coronavirus disease 2019. J Ultrasound Med 2020. https://doi.org/10.1002/jum.15571. 10.1002/jum.15571 Web of Science®Google Scholar 2Soldati G, Smargiassi A, Inchingolo R, et al. Proposal for international standardization of the use of lung ultrasound for patients with COVID-19: a simple, quantitative, reproducible method. J Ultrasound Med 2020; 39: 1413–1419. 10.1002/jum.15285 PubMedWeb of Science®Google Scholar 3Smargiassi A, Soldati G, Torri E, et al. Lung ultrasound for COVID-19 patchy pneumonia: extended or limited evaluations? J Ultrasound Med 2020. https://doi.org/10.1002/jum.15428. 10.1002/jum.15428 Web of Science®Google Scholar 4Perrone T, Soldati G, Padovini L, et al. A new lung ultrasound protocol able to predict worsening in patients affected by severe acute respiratory syndrome coronavirus 2 pneumonia. J Ultrasound Med 2020. https://doi.org/10.1002/jum.15548. 10.1002/jum.15548 Web of Science®Google Scholar 5Mento F, Perrone T, Macioce VN, et al. On the impact of different lung ultrasound imaging protocols in the evaluation of patients affected by coronavirus disease 2019: how many acquisitions are needed? J Ultrasound Med 2020. https://doi.org/10.1002/jum.15580. 10.1002/jum.15580 Web of Science®Google Scholar Citing Literature Volume40, Issue12December 2021Pages 2783-2783 ReferencesRelatedInformation

When assessing severity of COVID19 from lung ultrasound (LUS) frames, both anatomical phenomena (e.g., the pleural line, presence of consolidations), as well as sonographic artifacts, such as A-lines and B-lines are of importance. While ultrasound devices aim to provide an accurate visualization of the anatomy, the orientation of the sonographic artifacts differ between probe types. This difference poses a challenge in designing a unified deep artificial neural network capable of handling all probe types. In this work we improve upon Roy et al. (2020): We train a simple deep neural network to assess the severity of COVID-19 from LUS data. To address the challenge of handling both linear and convex probes in a unified manner we employed two strategies: First, we augment the input frames of convex probes with a “rectified” version in which A-lines and B-lines assume a horizontal/vertical aspect close to that achieved with linear probes. Second, we explicitly inform the network on the presence of important anatomical features and artifacts. We use a known Radon-based method for detecting the pleural line and B-lines and feed the detected lines as inputs to the network. Preliminary experiments yielded f1 = 68.7% compared to f1 = 65.1% reported by Roy et al.