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Optic nerve sheath diameter (ONSD) ultrasound is becoming increasingly more popular for estimating raised intracranial pressure (ICP). We performed a systematic review and analysis of the diagnostic accuracy of ONSD when compared to the standard invasive ICP measurement., Method: We performed a systematic search of PUBMED and EMBASE for studies including adult patients with suspected elevated ICP and comparing sonographic ONSD measurement to a standard invasive method. Quality of studies was assessed using the QUADAS-2 tool by two independent authors. We used a bivariate model of random effects to summarize pooled sensitivity, specificity, and diagnostic odds ratio (DOR). Heterogeneity was investigated by meta-regression and sub-group analyses., Results: We included 18 prospective studies (16 studies including 619 patients for primary outcome). Only one study was of low quality, and there was no apparent publication bias. Pooled sensitivity was 0.9 [95% confidence intervals (CI): 0.85-0.94], specificity was 0.85 (95% CI: 0.8-0.89), and DOR was 46.7 (95% CI: 26.2-83.2) with partial evidence of heterogeneity. The Area-Under-the-Curve of the summary Receiver-Operator-Curve was 0.93 (95% CI: 0.91-0.95, P < .05). No covariates were significant in the meta-regression. Subgroup analysis of severe traumatic brain injury and parenchymal ICP found no heterogeneity. ICP and ONSD had a correlation coefficient of 0.7 (95% CI: 0.63-0.76, P < .05)., Conclusion: ONSD is a useful adjunct in ICP evaluation but is currently not a replacement for invasive methods where they are feasible., (© 2021 American Institute of Ultrasound in Medicine.)

The evolution of ultrasound imaging into a key technology for diagnostic practice has resulted in its incorporation into the education of medical students worldwide. Although the introduction of ultrasound into medical schools' curricula is relatively recent, training of sonographers and other ultrasound users is mature. Ultrasound is being used in a variety of learning environments and clinical settings, from courses in anatomy and physiology to clinical rotations where medical and other students may scan healthy volunteers or patients, sometimes with little to no supervision. Educators may be apprehensive about a perceived high likelihood that students will encounter unexpected findings during these sessions, which could distress the patient or ultrasound model as well as the student, and result in problems that would be more pronounced if such incidental findings are complex. Policies are needed to address how to manage incidental ultrasound findings that are identified during educational activities. This article summarizes the background and provides a framework for establishing and implementing a well-designed and thoughtful approach for dealing with incidental findings observed in volunteer subjects by medical students during training courses in ultrasound diagnostic scanning. Subject confidentiality should be respected, and review of incidental findings should be transparent without provoking unnecessary anxiety. It is the responsibility of the instructor or supervisor to ensure adequate clinical follow-up if indicated., Competing Interests: Conflict of interest disclosure The authors report no conflict of interest., (Copyright © 2021 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Chest computed tomography has been frequently used to evaluate patients with potential coronavirus disease 2019 (COVID-19) infection. However, this may be particularly risky for pediatric patients owing to high doses of ionizing radiation. We sought to evaluate COVID-19 imaging options in pediatric patients based on the published literature. We performed an exhaustive literature review focusing on COVID-19 imaging in pediatric patients. We used the search terms "COVID-19," "SARS-CoV2," "coronavirus," "2019-nCoV," "Wuhan virus," "lung ultrasound (LUS)," "sonography," "lung HRCT," "children," "childhood" and "newborn" to query the online databases PubMed, Medical Subject Headings (MeSH), Embase, LitCovid, the World Health Organization COVID-19 database and Medline Bireme. Articles meeting the inclusion criteria were included in the analysis and review. We identified only seven studies using lung ultrasound (LUS) to diagnose severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in newborns and children. The studies evaluated small numbers of patients, and only 6% had severe or critical illness associated with COVID-19. LUS showed the presence of B-lines in 50% of patients, sub-pleural consolidation in 43.18%, pleural irregularities in 34.09%, coalescent B-lines and white lung in 25%, pleural effusion in 6.82% and thickening of the pleural line in 4.55%. We found 117 studies describing the use of chest X-ray or chest computed tomography in pediatric patients with COVID-19. The proportion of those who were severely or critically ill was similar to that in the LUS study population. Our review indicates that use of LUS should be encouraged in pediatric patients, who are at highest risk of complications from medical ionizing radiation. Increased use of LUS may be of particularly high impact in under-resourced areas, where access to chest computed tomography may be limited., (Copyright © 2021 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Scarce data exist regarding the natural history of lung lesions detected on ultrasound in those who survive severe COVID-19 pneumonia., Objective: We performed a prospective analysis of point-of-care ultrasound (POCUS) findings in critically ill COVID-19 patients during and after hospitalization., Methods: We enrolled 171 COVID-19 intensive care unit patients. POCUS of the lungs was performed with phased array (2-4 MHz), convex (2-6 MHz) and linear (10-15 MHz) transducers, scanning 12 lung areas. Chest computed tomography angiography was performed to exclude suspected pulmonary embolism. Survivors were clinically and sonographically evaluated during a 4 month period for evidence of residual lung injury. Chest computed tomography angiography and echocardiography were used to exclude pulmonary hypertension (PH) and chest high-resolution-computed-tomography to exclude interstitial lung disease (ILD) in symptomatic survivors., Results: Cox regression analysis showed that lymphocytopenia (hazard ratio [HR]: 0.88, 95% confidence intervals [CI]: 0.68-0.96, p = .048), increased lactate (HR: 1.17, 95% CI: 0.94-1.46, p = 0.049), and D-dimers (HR: 1.21, 95% CI: 1.03-1.44, p = .03) were mortality predictors. Non-survivors had increased incidence of pulmonary abnormalities (B-lines, pleural line irregularities, and consolidations) compared to survivors (p < .05). During follow-up, POCUS with clinical and laboratory parameters integrated in the semi-quantitative Riyadh-Residual-Lung-Injury scale had sensitivity of 0.82 (95% CI: 0.76-0.89) and specificity of 0.91 (95% CI: 0.94-0.95) in predicting ILD. The prevalence of PH and ILD (non-specific-interstitial-pneumonia) was 7% and 11.8%, respectively., Conclusion: POCUS showed ability to monitor the evolution of severe COVID-19 pneumonia after hospital discharge, supporting its integration in clinical predictive models of residual lung injury., (© 2020 American Institute of Ultrasound in Medicine.)

Objectives: To create a deep learning algorithm capable of video classification, using a long short-term memory (LSTM) network, to analyze collapsibility of the inferior vena cava (IVC) to predict fluid responsiveness in critically ill patients., Methods: We used a data set of IVC ultrasound (US) videos to train the LSTM network. The data set was created from IVC US videos of spontaneously breathing critically ill patients undergoing intravenous fluid resuscitation as part of 2 prior prospective studies. We randomly selected 90% of the IVC videos to train the LSTM network and 10% of the videos to test the LSTM network's ability to predict fluid responsiveness. Fluid responsiveness was defined as a greater than 10% increase in the cardiac index after a 500-mL fluid bolus, as measured by bioreactance., Results: We analyzed 211 videos from 175 critically ill patients: 191 to train the LSTM network and 20 to test it. Using standard data augmentation techniques, we increased our sample size from 191 to 3820 videos. Of the 175 patients, 91 (52%) were fluid responders. The LSTM network was able to predict fluid responsiveness moderately well, with an area under the receiver operating characteristic curve of 0.70 (95% confidence interval [CI], 0.43-1.00), a positive likelihood ratio of infinity, and a negative likelihood ratio of 0.3 (95% CI, 0.12-0.77). In comparison, point-of-care US experts using video review offline and manual diameter measurement via software caliper tools achieved an area under the receiver operating characteristic curve of 0.94 (95% CI, 0.83-0.99)., Conclusions: We demonstrated that an LSTM network can be trained by using videos of IVC US to classify IVC collapse to predict fluid responsiveness. Our LSTM network performed moderately well given the small training cohort but worse than point-of-care US experts. Further training and testing of the LSTM network with a larger data sets is warranted., (© 2020 American Institute of Ultrasound in Medicine.)

Background: Respiratory variation in carotid artery peak systolic velocity (ΔVpeak) assessed by point-of-care ultrasound (POCUS) has been proposed as a noninvasive means to predict fluid responsiveness. We aimed to evaluate the ability of carotid ΔVpeak as assessed by novice physician sonologists to predict fluid responsiveness., Methods: This study was conducted in 2 intensive care units. Spontaneously breathing, nonintubated patients with signs of volume depletion were included. Patients with atrial fibrillation/flutter, cardiogenic, obstructive or neurogenic shock, or those for whom further intravenous (IV) fluid administration would be harmful were excluded. Three novice physician sonologists were trained in POCUS assessment of carotid ΔVpeak. They assessed the carotid ΔVpeak in study participants prior to the administration of a 500 mL IV fluid bolus. Fluid responsiveness was defined as a ≥10% increase in cardiac index as measured using bioreactance., Results: Eighty-six participants were enrolled, 50 (58.1%) were fluid responders. Carotid ΔVpeak performed poorly at predicting fluid responsiveness. Test characteristics for the optimum carotid ΔVpeak of 8.0% were: area under the receiver operating curve = 0.61 (95% CI: 0.48-0.73), sensitivity = 72.0% (95% CI: 58.3-82.56), specificity = 50.0% (95% CI: 34.5-65.5)., Conclusions: Novice physician sonologists using POCUS are unable to predict fluid responsiveness using carotid ΔVpeak. Until further research identifies key limiting factors, clinicians should use caution directing IV fluid resuscitation using carotid ΔVpeak.

From its start in China in December 2019, infection by the new SARS-CoV2 spread fast all over the world. It can present as severe respiratory distress in the elderly or a vasculitis in a child, most of whom are typically completely asymptomatic. This makes infection detection based on clinical grounds exceedingly difficult. Lung ultrasound has become an important tool in diagnosis and follow-up of patient with COVID-19 infection.Here we review available, up to date literature on ultrasound use for COVID-19 suspected pediatric patients and contrast it to published findings in adult patients., (© 2020 American Institute of Ultrasound in Medicine.)

Point-of-care ultrasound (POCUS) use is rapidly expanding as a practice in adult and pediatric critical care environments. In January 2020, the Joint Commission endorsed a statement from the Emergency Care Research Institute citing point-of-care ultrasound as a potential hazard to patients for reasons related to training and skill verification, oversight of use, and recordkeeping and accountability mechanisms for clinical use; however, no evidence was presented to support these concerns. Existing data on point-of-care ultrasound practices in pediatric critical care settings verify that point-of-care ultrasound use continues to increase, and contrary to the concerns raised, resources are becoming increasingly available for point-of-care ultrasound use. Many institutions have recognized a successful approach to addressing these concerns that can be achieved through multispecialty collaborations., Competing Interests: Dr. Su received funding from the Society of Critical Care Medicine, Children’s Hospital of Philadelphia, and University of Texas Health Sciences San Antonio. Dr. Soni’s institution received funding from the Department of Veterans Affairs, Quality Enhancement Research Initiative Partnered Evaluation Initiative Grant (HX002263- 01A1). Dr. Blaivas received funding from Ethos Medical, SonoSim, EchoNous, and 410Medical. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2020 by the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies.)

Objectives: To perform a prospective longitudinal analysis of lung ultrasound findings in critically ill patients with coronavirus disease 2019 (COVID-19)., Methods: Eighty-nine intensive care unit (ICU) patients with confirmed COVID-19 were prospectively enrolled and tracked. Point-of-care ultrasound (POCUS) examinations were performed with phased array, convex, and linear transducers using portable machines. The thorax was scanned in 12 lung areas: anterior, lateral, and posterior (superior/inferior) bilaterally. Lower limbs were scanned for deep venous thrombosis and chest computed tomographic angiography was performed to exclude suspected pulmonary embolism (PE). Follow-up POCUS was performed weekly and before hospital discharge., Results: Patients were predominantly male (84.2%), with a median age of 43 years. The median duration of mechanical ventilation was 17 (interquartile range, 10-22) days; the ICU length of stay was 22 (interquartile range, 20.2-25.2) days; and the 28-day mortality rate was 28.1%. On ICU admission, POCUS detected bilateral irregular pleural lines (78.6%) with accompanying confluent and separate B-lines (100%), variable consolidations (61.7%), and pleural and cardiac effusions (22.4% and 13.4%, respectively). These findings appeared to signify a late stage of COVID-19 pneumonia. Deep venous thrombosis was identified in 16.8% of patients, whereas chest computed tomographic angiography confirmed PE in 24.7% of patients. Five to six weeks after ICU admission, follow-up POCUS examinations detected significantly lower rates (P < .05) of lung abnormalities in survivors., Conclusions: Point-of-care ultrasound depicted B-lines, pleural line irregularities, and variable consolidations. Lung ultrasound findings were significantly decreased by ICU discharge, suggesting persistent but slow resolution of at least some COVID-19 lung lesions. Although POCUS identified deep venous thrombosis in less than 20% of patients at the bedside, nearly one-fourth of all patients were found to have computed tomography-proven PE., (© 2020 American Institute of Ultrasound in Medicine.)

The analysis of vertical reverberation artefacts is an essential component of the differential diagnosis in pulmonary ultra-sound. Traditionally, they are often, but not exclusively, called B-line artefacts (BLA) and/or comet tail artefacts (CTA), but this view is misleading. In this position paper we clarify the terminology and relation of the two lung reverberation artefacts BLA and CTA to spe-cific clinical scenarios. BLA are defined by a normal pleura line and are a typical hallmark of cardiogenic pulmonary edema after exclusion of certain pathologies including pneumonia or lung contusion, whereas CTAs show an irregular pleura line representing a variety of parenchymal lung diseases. The dual approach using low frequency transducers to determine BLA and high frequency transducer to determine the pleural surface is recommended.

Objectives: To evaluate the accuracy of a new COVID-19 prognostic score based on lung ultrasound (LUS) and previously validated variables in predicting critical illness., Methods: We conducted a single-center retrospective cohort development and internal validation study of the COVID-19 Worsening Score (COWS), based on a combination of the previously validated COVID-GRAM score (GRAM) variables and LUS. Adult COVID-19 patients admitted to the emergency department (ED) were enrolled. Ten variables previously identified by GRAM, days from symptom onset, LUS findings, and peripheral oxygen saturation/fraction of inspired oxygen (P/F) ratio were analyzed. LUS score as a single predictor was assessed. We evaluated GRAM model's performance, the impact of adding LUS, and then developed a new model based on the most predictive variables., Results: Among 274 COVID-19 patients enrolled, 174 developed critical illness. The GRAM score identified 51 patients at high risk of developing critical illness and 132 at low risk. LUS score over 15 (range 0 to 36) was associated with a higher risk ratio of critical illness (RR, 2.05; 95% confidence interval [CI], 1.52-2.77; area under the curve [AUC], 0.63; 95% CI 0.676-0.634). The newly developed COVID-19 Worsening Score relies on five variables to classify high- and low-risk patients with an overall accuracy of 80% and negative predictive value of 93% (95% CI, 87%-98%). Patients scoring more than 0.183 on COWS showed a RR of developing critical illness of 8.07 (95% CI, 4.97-11.1)., Conclusions: COWS accurately identify patients who are unlikely to need intensive care unit (ICU) admission, preserving resources for the remaining high-risk patients., (© 2021 Wiley Periodicals LLC.)

Objectives: Deep learning for medical imaging analysis uses convolutional neural networks pretrained on ImageNet (Stanford Vision Lab, Stanford, CA). Little is known about how such color- and scene-rich standard training images compare quantitatively to medical images. We sought to quantitatively compare ImageNet images to point-of-care ultrasound (POCUS), computed tomographic (CT), magnetic resonance (MR), and chest x-ray (CXR) images., Methods: Using a quantitative image quality assessment technique (Blind/Referenceless Image Spatial Quality Evaluator), we compared images based on pixel complexity, relationships, variation, and distinguishing features. We compared 5500 ImageNet images to 2700 CXR, 2300 CT, 1800 MR, and 18,000 POCUS images. Image quality results ranged from 0 to 100 (worst). A 1-way analysis of variance was performed, and the standardized mean-difference effect size value (d) was calculated., Results: ImageNet images showed the best image quality rating of 21.7 (95% confidence interval [CI], 0.41) except for CXR at 13.2 (95% CI, 0.28), followed by CT at 35.1 (95% CI, 0.79), MR at 31.6 (95% CI, 0.75), and POCUS at 56.6 (95% CI, 0.21). The differences between ImageNet and all of the medical images were statistically significant (P ≤ .000001). The greatest difference in image quality was between ImageNet and POCUS (d = 2.38)., Conclusions: Point-of-care ultrasound (US) quality is significantly different from that of ImageNet and other medical images. This brings considerable implications for convolutional neural network training with medical images for various applications, which may be even more significant in the case of US images. Ultrasound deep learning developers should consider pretraining networks from scratch on US images, as training techniques used for CT, CXR, and MR images may not apply to US., (© 2020 American Institute of Ultrasound in Medicine.)

Background: Recent studies have shown an increased prevalence of thromboembolic disease in critically ill patients with the novel SARS-CoV-2 disease (COVID-19). However, the use of enhanced anticoagulation therapy in these patients remains controversial., Objectives: To determine the incidence of thromboembolic phenomena (TEP) and hemorrhagic events (HEs) in intensive care unit (ICU) COVID-19 patients., Methods: One hundred and sixty ICU patients with COVID-19 were enrolled. Clinical examination results, laboratory data, and imaging studies (computed tomography/Doppler ultrasound scans) for these patients were retrospectively collected and analyzed. Outcome measures including days on mechanical ventilation, ICU length of stay, and day-28 mortality were recorded., Results: Sixty patients (37.5%) developed TEP including thirty patients with deep vein thrombosis, 55 patients with pulmonary embolism, and 7 patients with arterial thromboembolism. Cardiac arrhythmias, lymphocytopenia, and increased D-dimers were more frequently observed in the TEP group compared to the non-TEP group of patients (all p < 0.05). The sensitivity, specificity, and positive and negative predictive values of a cutoff D-dimer level of 3.0  μ g/mL for predicting PE were 74.5%, 95.1%, 86.8%, and 91.9%, respectively. Thirteen patients experienced HEs, which were more frequently observed in the TEP group ( p < 0.05). Twenty-eight-day mortality was higher in the TEP group (60%) compared to the non-TEP group (30%) of patients ( p =0.02)., Conclusions: The rates of TEP and HEs in mechanically ventilated critically ill COVID-19 patients were 37. 5% and 8.1%. Twenty-eight-day mortality was higher in the TEP group (60%) compared to the non-TEP group (30%) of patients., Competing Interests: FF, AA, AB, NN, KA, FA, ZAM, SAA, and DK declare that there are no conflicts of interest. MB consults for EthosMedical, 410Medical, EchoNous, and Sonosim; none of these companies were aware of the study or had influence on it., (Copyright © 2021 Fahad Faqihi et al.)

This study sought to compare point-of-care ultrasound (POCUS) and conventional X-rays for detecting fractures in children. This was a prospective, non-randomized, convenience-sample study conducted in five medical centers. It evaluated pediatric patients with trauma. POCUS and X-ray examination results were treated as dichotomous variables with fracture either present or absent. Descriptive statistics were calculated in addition to prevalence, sensitivity, specificity, positive predictive value and negative predictive value, including 95% confidence intervals (CIs). The Cohen κ coefficient was determined as a measurement of the level of agreement. A total of 554 examinations were performed with POCUS and X-ray. On physical examination, swelling, localized hematoma and functional limitation were found in 66.73%, 33.78% and 53.74% of participants, respectively. The most-studied areas were limbs and hands/feet (58.19% and 38.27%), whereas the thorax was less represented (3.54%). Sensitivity of POCUS was 91.67% (95% CI, 76.41-97.82%) for high-skill providers and 71.50 % (95% CI, 64.75-77.43%) for standard-skill providers. Specificity was 88.89% (95% CI, 73.00-96.34%) and 82.91% (95% CI, 77.82-87.06%) for high- and standard-skill providers, respectively. Positive predictive value was 89.19% (95% CI, 73.64-96.48%) and 75.90% (95% CI, 69.16-81.59%) for high- and standard-skill providers, respectively. Negative predictive value was 91.43% (95% CI, 75.81-97.76%) and 79.44% (95% CI, 74.21-83.87%) for high- and standard-skill providers, respectively. The Cohen κ coefficient showed very good agreement (0.81) for high-skill providers, but moderate agreement (0.54) for standard-skill providers. We noted good diagnostic accuracy of POCUS in evaluating fracture, with excellent sensitivity, specificity, and positive and negative predictive value for high-skill providers., Competing Interests: Conflict of interest disclosure The authors declare that they have no conflict of interest. There is no funding source for this study., (Copyright © 2020 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Objectives: Inferior vena cava collapsibility (cIVC) measured by point-of-care ultrasound (POCUS) has been proposed as a noninvasive means of assessing fluid responsiveness. We aimed to prospectively evaluate the performance of a 25% cIVC cutoff value to detect fluid responsiveness among spontaneously breathing intensive care unit (ICU) patients when assessed with POCUS by novice versus expert physician sonologists., Methods: Prospective observational study of spontaneously breathing ICU patients. Fluid responsiveness was defined as a > 10% increase in cardiac index following a 500 mL fluid bolus, measured by bioreactance. Novice sonologist measured cIVC with POCUS. Their measurements were later compared to an expert physician sonologist who independently reviewed the POCUS images and assessed cIVCs., Results: Of the 85 participants, 44 (52%) were fluid responders. A 25% cIVC cutoff value performed better when assessed by expert sonologists than novice physician sonologists (receiver-operator characteristic curve, ROC = 0.82 [0.74-0.88] vs ROC = 0.69 [0.60-0.77])., Conclusions: A 25% cIVC cutoff value measured by POCUS detects fluid responsiveness. However, the experience of the physician sonologist affects test performance and should be considered when interpreting and clinically using cIVC to direct intravenous fluid resuscitation.

Rapid adoption and widespread use of point-of-care ultrasound (POCUS) has impacted diagnostic testing and clinical care across medical disciplines. The benefits of POCUS must be weighed against certain pitfalls, such as the risk of misdiagnosis and false assurance. Beyond technical error in image acquisition and interpretation, an important pitfall is reliance on POCUS results without considering pre-test patient characteristics or the diagnostic accuracy of POCUS in varying clinical contexts. In this article, we introduce the concept of POCUS stewardship that emphasizes critical evaluation of clinical indications prior to performing POCUS as well as the individual patient and test characteristics of POCUS when integrating results into clinical decisionmaking. Adherence to these principles can lead to optimized POCUS application and improved patient care., Competing Interests: The authors have no conflict of interest to disclose., (© 2020 The Authors. JACEP Open published by Wiley Periodicals LLC on behalf of American College of Emergency Physicians.)

Measurement of carotid blood flow (CBF) and corrected carotid flow time (ccFT) has been proposed as a non-invasive means of determining fluid responsiveness. We evaluated the ability of CBF and ccFT as assessed by novice sonologists to determine fluid responsiveness in intensive care unit patients. Three novice physician sonologists performed carotid ultrasounds before and after a fluid bolus and calculated changes in CBF and ccFT. Fluid responsiveness was defined as a ≥10% increase in cardiac index as measured using bioreactance. Of 112 participants, 56 (50%) were fluid responders. Changes in CBF and ccFT performed poorly at determining fluid responsiveness: 19 mL/min (area under the receiver operating characteristic curve: 0.58, 95% confidence interval: 0.47-0.68) and 6 ms (0.59, 0.46-0.65) respectively. Novice physician sonologists are unable to determine fluid responsiveness using CBF or ccFT. Further research is needed to identify the key limiting factors in using carotid ultrasound to determine fluid responsiveness., (Copyright © 2020 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Objectives: We sought to create a deep learning (DL) algorithm to identify vessels, bones, nerves, and tendons on transverse upper extremity (UE) ultrasound (US) images to enable providers new to US-guided peripheral vascular access to identify anatomy., Methods: We used publicly available DL architecture (YOLOv3) and deidentified transverse US videos of the UE for algorithm development. Vessels, bones, tendons, and nerves were labeled with bounding boxes. A total of 203,966 images were generated from videos, with corresponding label box coordinates in a YOLOv3 format. Training accuracy, losses, and learning curves were tracked. As a final real-world test, 50 randomly selected images from unrelated UE US videos were used to test the DL algorithm. Four different versions of the YOLOv3 algorithm were tested with varied amounts of training and sensitivity settings. The same 50 images were labeled by 2 blinded point-of-care ultrasound (POCUS) experts. The area under the curve (AUC) was calculated for the DL algorithm and POCUS expert performance., Results: The algorithm outperformed POCUS experts in detection of all structures in the UE, with an AUC of 0.78 versus 0.69 and 0.71, respectively. When considering vessels, only one of the POCUS experts attained an AUC of 0.85, just ahead of the DL algorithm, with an AUC of 0.83., Conclusions: Our DL algorithm proved accurate at identifying 4 common structures on cross-sectional US imaging of the UE, which would allow novice POCUS providers to more confidently and accurately target vessels for cannulation, avoiding other structures. Overall, the algorithm outperformed 2 blinded POCUS experts., (© 2020 by the American Institute of Ultrasound in Medicine.)

Focused transthoracic echocardiography (TTE) during cardiac arrest resuscitation can enable the characterization of myocardial activity, identify potentially treatable pathologies, assist with rhythm interpretation, and provide prognostic information. However, an important limitation of TTE is the difficulty obtaining interpretable images due to external and patient-related limiting factors. Over the last decade, focused transesophageal echocardiography (TEE) has been proposed as a tool that is ideally suited to image patients in extremis-those in cardiac arrest and periarrest states. In addition to the same diagnostic and prognostic role provided by TTE images, TEE provides unique advantages including the potential to optimize the quality of chest compressions, shorten cardiopulmonary resuscitation interruptions, guide resuscitative procedures, and provides a continuous image of myocardial activity. This review discusses the rationale, supporting evidence, opportunities, and challenges, and proposes a research agenda for the use of focused TEE in cardiac arrest with the goal to improve resuscitation outcomes., (Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Objectives: We sought to create a deep learning algorithm to determine the degree of inferior vena cava (IVC) collapsibility in critically ill patients to enable novice point-of-care ultrasound (POCUS) providers., Methods: We used publicly available long short term memory (LSTM) deep learning basic architecture that can track temporal changes and relationships in real-time video, to create an algorithm for ultrasound video analysis. The algorithm was trained on public domain IVC ultrasound videos to improve its ability to recognize changes in varied ultrasound video. A total of 220 IVC videos were used, 10% of the data was randomly used for cross correlation during training. Data were augmented through video rotation and manipulation to multiply effective training data quantity. After training, the algorithm was tested on the 50 new IVC ultrasound video obtained from public domain sources and not part of the data set used in training or cross validation. Fleiss' κ was calculated to compare level of agreement between the 3 POCUS experts and between deep learning algorithm and POCUS experts., Results: There was very substantial agreement between the 3 POCUS experts with κ = 0.65 (95% CI = 0.49-0.81). Agreement between experts and algorithm was moderate with κ = 0.45 (95% CI = 0.33-0.56)., Conclusions: Our algorithm showed good agreement with POCUS experts in visually estimating degree of IVC collapsibility that has been shown in previously published studies to differentiate fluid responsive from fluid unresponsive septic shock patients. Such an algorithm could be adopted to run in real-time on any ultrasound machine with a video output, easing the burden on novice POCUS users by limiting their task to obtaining and maintaining a sagittal proximal IVC view and allowing the artificial intelligence make real-time determinations., Competing Interests: MB consults with EchoNous Inc., Sonosim Inc. Ethos Medical and 410Medical. None of the companies had influence or contribution to this study and article or knowledge of its performance., (© 2020 The Authors. JACEP Open published by Wiley Periodicals LLC on behalf of the American College of Emergency Physicians.)

Coronavirus disease 2019 (COVID-19) has created unprecedented disruption for global healthcare systems. Offices and emergency departments (EDs) were the first responders to the pandemic, followed by medical wards and intensive care unit (ICUs). Worldwide efforts sprouted to coordinate proper response by increasing surge capacity and optimizing diagnosis and containment. Within the complex scenario of the outbreak, the medical community shared scientific research and implemented best-guess imaging strategies in order to save time and additional staff exposures. Early publications showed agreement between chest computed tomography (CT) and lung sonography: widespread ground-glass findings resembling acute respiratory distress syndrome (ARDS) on CT of COVID-19 patients matched lung ultrasound signs and patterns. Well-established accuracy of bedside sonography for lung conditions and its advantages (such as no ionizing radiation; low-cost, real-time bedside imaging; and easier disinfection steps) prompted a wider adoption of lung ultrasound for daily assessment and monitoring of COVID-19 patients. Growing literature, webinars, online materials, and international networks are promoting lung ultrasound for the same purpose. We propose 11 lung ultrasound roles for different medical settings during the pandemic, starting from the out-of-hospital setting, where lung ultrasound has ergonomic and infection control advantages. Then we describe how medical wards and ICUs can safely integrate lung ultrasound into COVID-19 care pathways. Finally, we present outpatient use of lung ultrasound to aid follow-up of positive case contacts and of those discharged from the hospital., Competing Interests: Enrico Boero, Annia Schreiber, Serena Rovida, and Michael Blaivas have no competing interests to declare. Luigi Vetrugno received travel support for Congress Lecture by Cook Medical., (© 2020 The Authors. JACEP Open published by Wiley Periodicals LLC on behalf of the American College of Emergency Physicians.)

COVID-19 is proving to be a devastating pandemic with both tragic economic and health consequences worldwide. Point-of-care ultrasound (POCUS) of the lungs has been thrust into the forefront of resources that could be used in the management of COVID-19 acute care patients. However, relatively little attention has been paid to POCUS utility in assessing the heart in COVID-19 patients. Anecdotal reports suggest encounters of likely COVID-19 induced pericardial effusions and myocardial electrical dysfunction. This article presents 2 cases of generally healthy patients who were noted to have classic COVID-19 bilateral pneumonia findings on lung ultrasound and incidentally discovered to have unsuspected left ventricular dysfunction likely resulting from myocarditis. POCUS videos are presented as illustrations of this potentially overlooked complication., Competing Interests: MB is a consultant with EchoNous Inc, 410Medical, Ethos Medical and Sonosim Inc. These companies had no influence or contribution to this manuscript, nor knowledge of its creation., (© 2020 The Authors. JACEP Open published by Wiley Periodicals LLC on behalf of the American College of Emergency Physicians.)

Objectives: Little is known about optimal deep learning (DL) approaches for point-of-care ultrasound (POCUS) applications. We compared 6 popular DL architectures for POCUS cardiac image classification to determine whether an optimal DL architecture exists for future DL algorithm development in POCUS., Methods: We trained 6 convolutional neural networks (CNNs) with a range of complexities and ages (AlexNet, VGG-16, VGG-19, ResNet50, DenseNet201, and Inception-v4). Each CNN was trained by using images of 5 typical POCUS cardiac views. Images were extracted from 225 publicly available deidentified POCUS cardiac videos. A total of 750,018 individual images were extracted, with 90% used for model training and 10% for cross-validation. The training time and accuracy achieved were tracked. A real-world test of the algorithms was performed on a set of 125 completely new cardiac images. Descriptive statistics, Pearson R values, and κ values were calculated for each CNN., Results: Accuracy ranged from 96% to 85.6% correct for the 6 CNNs. VGG-16, one of the oldest and simplest CNNs, performed best at 96% correct with 232 minutes to train (R = 0.97; κ = 0.95; P < .00001). The worst-performing CNN was the newer DenseNet201, with 85.6% accuracy and 429 minutes to train (R = 0.92; κ = 0.82; P < .00001)., Conclusions: Six common image classification DL algorithms showed considerable variability in their accuracy and training time when trained and tested on identical data, suggesting that not all will perform optimally for POCUS DL applications. Contrary to well-established accuracies for CNNs, more modern and deeper algorithms yielded poorer results., (© 2019 by the American Institute of Ultrasound in Medicine.)

Ultrasound is becoming a fundamental first-line diagnostic tool for most medical specialties and an innovative tool to teach anatomy, physiology and pathophysiology to undergraduate and graduate students. However, availability of structured training programs during medical school is lagging behind and many physicians still acquire all their ultrasound skills during postgraduate training.There is wide variation in medical student ultrasound education worldwide. Sharing successful educational strategies from early adopter medical schools and learning from leading education programs should advance the integration of ultrasound into the university medical school curricula. In this overview, we present current approaches and suggestions by ultrasound societies concerning medical student educa-tion throughout the world. Based on these examples, we formulate a consensus statement with suggestions on how to integrate ultrasound teaching into the preclinical and clinical medical curricula.

Point-of-care ultrasound (POCUS) equipment management is critical in optimizing daily clinical operations in emergency departments (EDs). Traditional consultative ultrasound laboratories are well practiced at operations management, but this is not the case for POCUS programs, because machine upgrade and replacement metrics have not been developed or tested. We present a data-driven method for assessment of POCUS equipment maintenance and replacement named the ULTrA (a data-driven approach to point-of-care ultrasound upgrade) score. This novel model of assessing each ultrasound machine by quantitative scoring in each of four mostly objective categories: use (U), likeability (L), trustworthiness (Tr), and age (A). We propose the ULTrA model as a method to identify underperforming devices which could be upgraded or eliminated, and to compare relative performance amongst a group of departmental ultrasound machines. This composite score may be a useful objective tool that could replace individual proxies for clinical effectiveness, such as age, use, or individual provider preference. Additional research in multiple centers would be needed to refine and validate the ULTrA score. Once fully developed, the ULTrA score could be deployed in EDs and other clinical settings where POCUS is used to help streamline resources to maintain a functional and state-of-the-art fleet of ultrasound machines over time., Competing Interests: The authors declare no conflict of interest., (© 2020 The Authors. JACEP Open published by Wiley Periodicals LLC on behalf of the American College of Emergency Physicians.)

Background: Artificial intelligence (AI) is increasingly a part of daily life and offers great possibilities to enrich health care. Imaging applications of AI have been mostly developed by large, well-funded companies and currently are inaccessible to the comparatively small market of point-of-care ultrasound (POCUS) programs. Given this absence of commercial solutions, we sought to create and test a do-it-yourself (DIY) deep learning algorithm to classify ultrasound images to enhance the quality assurance work-flow for POCUS programs., Methods: We created a convolutional neural network using publicly available software tools and pre-existing convolutional neural network architecture. The convolutional neural network was subsequently trained using ultrasound images from seven ultrasound exam types: pelvis, heart, lung, abdomen, musculoskeletal, ocular, and central vascular access from 189 publicly available POCUS videos. Approximately 121,000 individual images were extracted from the videos, 80% were used for model training and 10% each for cross validation and testing. We then tested the algorithm for accuracy against a set of 160 randomly extracted ultrasound frames from ultrasound videos not previously used for training and that were performed on different ultrasound equipment. Three POCUS experts blindly categorized the 160 random images, and results were compared to the convolutional neural network algorithm. Descriptive statistics and Krippendorff alpha reliability estimates were calculated., Results: The cross validation of the convolutional neural network approached 99% for accuracy. The algorithm accurately classified 98% of the test ultrasound images. In the new POCUS program simulation phase, the algorithm accurately classified 70% of 160 new images for moderate correlation with the ground truth, α = 0.64. The three blinded POCUS experts correctly classified 93%, 94%, and 98% of the images, respectively. There was excellent agreement among the experts with α = 0.87. Agreement between experts and algorithm was good with α = 0.74. The most common error was misclassifying musculoskeletal images for both the algorithm (40%) and POCUS experts (40.6%). The algorithm took 7 minutes 45 seconds to review and classify the new 160 images. The 3 expert reviewers took 27, 32, and 45 minutes to classify the images, respectively., Conclusions: Our algorithm accurately classified 98% of new images, by body scan area, related to its training pool, simulating POCUS program workflow. Performance was diminished with exam images from an unrelated image pool and ultrasound equipment, suggesting additional images and convolutional neural network training are necessary for fine tuning when using across different POCUS programs. The algorithm showed theoretical potential to improve workflow for POCUS program directors, if fully implemented. The implications of our DIY AI for POCUS are scalable and further work to maximize the collaboration between AI and POCUS programs is warranted., Competing Interests: MB consults with EchoNous Inc and 410Medical. Neither company had influence or contribution to this study and manuscript, nor knowledge of its performance., (© 2020 The Authors. JACEP Open published by Wiley Periodicals, Inc. on behalf of the American College of Emergency Physicians.)

Despite its limitations, conventional radiography is the method of choice for fracture evaluation in the emergency department. Only a few studies, moreover in limited populations, have evaluated the possible benefits of ultrasound (US), and especially of point-of-care ultrasound (POCUS), in the diagnosis of fractures. We sought to compare the accuracy of POCUS with that of conventional radiography in the diagnosis of bone fractures. This prospective study with a non-randomly allocated convenience sample was conducted at two academic medical centers. Four physicians, with focused training in musculoskeletal POCUS, evaluated consecutive patients with suspected orthopedic injury. US and X-ray examination results were treated as dichotomous variables with either fracture present or fracture absent. Descriptive statistics were calculated in addition to prevalence, sensitivity, specificity, positive predictive value and negative predictive value including 95% confidence intervals (CIs). Cohen's κ coefficient was determined as a measurement of the level of agreement. Four hundred sixty-nine patients (404 adult and 65 pediatric) ranging in age from 1-97 y were enrolled at two different hospitals. Seven hundred six examinations, both US and X-ray, were performed in 634 suspected fractures in adults (age ≥18 y) and 72 in children. On physical examination, swelling, localized hematoma and functional limitation were found in 64.61%, 34.97% and 53.52, respectively. The sensitivity of US examination was 93.89% (CI: 89.74%-96.49%) for all patients and 94.30% (CI: 89.77%-96.98%) and 91.67% (CI: 76.41%-97.82%) in adult and pediatric groups, respectively. Specificity was 94.13% (CI: 91.53-95.99), 94.56% (CI: 91.89-96.41) and 88.89% (CI: 73.00-96.38) for the whole group, adults and children, respectively. The positive predictive value was 88.48% (CI: 83.62%-92.08%), 88.35% (CI: 82.97%-92.24%) and 89.19% (CI: 73.64%-96.48%) for the whole group, adults and children, respectively. The negative predictive value was 96.98% (CI: 94.86%-98.27%), 97.43% (CI: 95.31%-98.64%) and 91.43% (CI: 75.81%-97.76%) in the three groups, respectively. Cohen's κ coefficient revealed high agreement of 0.87 for both the whole group and adult patients and 0.81 for pediatric patients. We found that POCUS has significant diagnostic accuracy in evaluating fracture compared with plain radiography, with excellent sensitivity, specificity and positive and negative predictive values., (Copyright © 2019 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Competing Interests: The authors have disclosed that they do not have any potential conflicts of interest.

Recent applications of artificial intelligence (AI) and deep learning (DL) in health care include enhanced diagnostic imaging modalities to support clinical decisions and improve patients' outcomes. Focused on using automated DL-based systems to improve point-of-care ultrasound (POCUS), we look at DL-based automation as a key field in expanding and improving POCUS applications in various clinical settings. A promising additional value would be the ability to automate training model selections for teaching POCUS to medical trainees and novice sonologists. The diversity of POCUS applications and ultrasound equipment, each requiring specialized AI models and domain expertise, limits the use of DL as a generic solution. In this article, we highlight the most advanced potential applications of AI in POCUS tailored to high-yield models in automated image interpretations, with the premise of improving the accuracy and efficacy of POCUS scans., (© 2018 by the American Institute of Ultrasound in Medicine.)

Over the past two decades, ultrasound (US) has become widely accepted to guide safe and accurate insertion of vascular devices in critically ill patients. We emphasize central venous catheter insertion, given its broad application in critically ill patients, but also review the use of US for accessing peripheral veins, arteries, the medullary canal, and vessels for institution of extracorporeal life support. To ensure procedural safety and high cannulation success rates we recommend using a systematic protocolized approach for US-guided vascular access in elective clinical situations. A standardized approach minimizes variability in clinical practice, provides a framework for education and training, facilitates implementation, and enables quality analysis. This review will address the state of US-guided vascular access, including current practice and future directions.

[This corrects the article on p. 649 in vol. 19, PMID: 30013699.].

Background: Excessive extravascular lung water (EVLW) is associated with increased morbidity and mortality. We compared three lung-ultrasound (L-US) techniques against the reference-standard transpulmonary thermodilution (TPTD) technique to access EVLW., Materials and Methods: This was a prospective, single-blind, cross-sectional study. Forty-four septic patients were enrolled. EVLW index was measured by the TPTD method, and an index of ≥10 mL/kg was considered diagnostic of pulmonary edema. EVLW index was then compared to three established bedside L-US protocols that evaluate sonographic B-lines: (1) a 28-zone protocol (total B-line score [TBS]) (2) a scanning 8-region examination, and (3) a 4-point examination., Results: Eighty-nine comparisons were obtained. A statistically significant positive correlation was found between L-US TBS and an EVLW index ≥10 mL/kg ( r = 0.668,P < 0.001). The 28-zone protocol score ≥39 has a sensitivity of 81.6% and a specificity of 76.5% to define EVLW index ≥10 mL/kg. In contrast, the positive 4-point examination and scanning 8-regions showed low sensitivity (23.7% and 50.0%, respectively) but high specificity (96.1% and 88.2%, respectively). Ten patients with a total of 21 comparisons met criteria for acute respiratory distress syndrome (ARDS). In this subgroup, only the TBS had statistically significant positive correlation to EVLW ( r = 0.488,P = 0.025)., Conclusion: L-US is feasible in patients with severe sepsis. In addition, L-US 28-zone protocol demonstrated high specificity and better sensitivity than abbreviated 4- and 8-zone protocols. In ARDS, the L-US 28-zone protocol was more accurate than the 4- and 8-zone protocols in predicting EVLW. Consideration of limitations of the latter protocols may prevent clinicians from reaching premature conclusions regarding the prediction of EVLW., Trial Registration: ISRCTN11419081. Registered 4 February 2015 retrospectively., Competing Interests: There are no conflicts of interest.

Background: Lung ultrasound and echocardiography are mainly applied in critical care and emergency medicine. However, the diagnostic value of cardiopulmonary ultrasound in elderly patients with acute respiratory distress syndrome (ARDS) is still unclear., Methods: Consecutive patients admitted to ICU with the diagnosis of suspected ARDS based on clinical grounds were enrolled. Cardiopulmonary ultrasound was performed as part of monitoring on day 1, day 2 and day 3. On each day a bedside ultrasound was performed to examine the lungs and calculate the Left Ventricular Ejection Fraction (LVEF). On day 3, a thoracic CT was performed on each patient as gold standard for ARDS imaging diagnosis. According to the results from CT scan, patients were grouped into ARDS group or Non-ARDS group. The relation between the cardiopulmonary ultrasound results on each day and the results of CT scan was analyzed., Results: Fifty one consecutive patients aged from 73 to 97 years old were enrolled. Based on CT criteria, 33 patients were classified into the ARDS group, while 18 patients were included in non-ARDS group. There was no significant difference between the two groups in baseline characteristics, including gender, age, underlying disease, comorbidities, APACHE II score, SOFA score, and PaO2/FiO2 ratio (P > 0.05). Lung ultrasound (LUS) examination results were consistent with the CT scan results in diagnosis of pulmonary lesions. The Kappa values were 0.55, 0.74 and 0.82 on day 1, day 2 and day 3, respectively. The ROC analysis showed that the sensitivity, specificity and area under curve of ROC (AUROC) for lung ultrasound in diagnose ARDS were 0.788,0.778,0.783;0.909,0.833,0.871;0.970,0.833,0.902 on day 1, day 2 and day 3, respectively. However, cardiopulmonary ultrasound performed better in diagnosing ARDS in elderly patients. The sensitivity, specificity and AUROC were 0.879,0.889,0.924;0.939,0.889,0.961;and 0.970,0.833,0.956 on day 1, day 2 and day 3, respectively. The combined performances of cardiopulmonary ultrasound, N-terminal pro-brain natriuretic peptide (NT-proBNP), and PaO2/FiO2 ratio improved the specificity of the diagnosis of ARDS in elderly patients., Conclusions: LUS examination results were consistent with the CT scan results in diagnosis of pulmonary lesions. Cardiopulmonary ultrasound has a greater diagnostic accuracy in elderly patients with ARDS, compared with LUS alone. The combined performances of cardiopulmonary ultrasound, NT-proBNP, and PaO 2 /FiO 2 increased the specificity of the diagnosis of ARDS in elderly patients.

Clinical ultrasound (CUS) is integral to the practice of an increasing number of medical specialties. Guidelines are needed to ensure effective CUS utilization across health systems. Such guidelines should address all aspects of CUS within a hospital or health system. These include leadership, training, competency, credentialing, quality assurance and improvement, documentation, archiving, workflow, equipment, and infrastructure issues relating to communication and information technology. To meet this need, a group of CUS subject matter experts, who have been involved in institution- and/or systemwide clinical ultrasound (SWCUS) program development convened. The purpose of this paper was to create a model for SWCUS development and implementation., Competing Interests: Conflicts of Interest: By the WestJEM article submission agreement, all authors are required to disclose all affiliations, funding sources and financial or management relationships that could be perceived as potential sources of bias. No author has professional or financial relationships with any companies that are relevant to this study. There are no conflicts of interest or sources of funding to declare.

Cardiac arrest is one of the most challenging patient presentations managed by emergency care providers, and echocardiography can be instrumental in the diagnosis, prognosis, and treatment guidance in these critically ill patients. Transesophageal echocardiography has many advantages over transthoracic echocardiography in a cardiac arrest resuscitation. As transesophageal echocardiography is implemented more widely at the point of care during cardiac arrest resuscitations, guidelines are needed to assist emergency providers in acquiring the equipment and skills necessary to successfully incorporate it into the management of cardiac arrest victims., (Copyright © 2017 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.)

Objectives: Point-of-care ultrasound (US) is an emerging tool used by pediatric emergency physicians in the last decade. Currently in the Middle East, point-of-care US use and education are at an early stage, with no designed curriculum or guidelines for its implementation in pediatric emergency medicine (EM). The objective of this article is to describe the clinical and educational uses of point-of-care US among certified pediatric EM physicians., Methods: A 19-question survey was sent to all certified pediatric emergency physicians and fellows in pediatric emergency fellowships in Saudi Arabia in February 2016. Reminders were sent weekly for 4 weeks., Results: The response rate was 84 of 88 (95%). Fifty-one of 84 (61%) reported using point-of-care US. Focused assessment with sonography for trauma was the most frequent use of point-of-care US (37%), followed by procedures (19%). The most common barrier for not using point-of-care US was limited training (86%). The most preferred tool for point-of-care US teaching was courses by EM physicians. Currently, there is no specific curriculum designed for pediatric EM in the Middle East., Conclusions: Despite the multiple applications of point-of-care US in pediatric EM, its use is still limited. Formal point-of-care US training with bedside sessions and courses was the mort preferred method of education. A designed curriculum needs to be implemented in pediatric emergency fellowships in Saudi Arabia., (© 2017 by the American Institute of Ultrasound in Medicine.)

Objective: To explore the potential role of ultrasound (US) as a bedside tool in intensive care unit patients with presumed neurogenic heterotopic ossification (NHO)., Setting: Tertiary university teaching hospitals., Participants: Critical care patients with acquired brain injury., Design: A multicenter prospective study of critical care patients with possible NHO., Main Measures: Following a screening clinical examination, the major joints were evaluated with US, serum laboratory work, and x-ray., Results: A total of 310 patients had decreased range of motion by clinical screen and all received US interrogation. Sonographic signs of NHO were identified in 21 of the 310 patients (6.8%), of which 17/21 (81.0%) showed hyperemia; 9/21 (42.8%) showed the reverse zone phenomenon, and 4/21 patients (19.0%) showed the prezone phenomenon. All 21 patients progressed to the zone phenomenon within 15 weeks. Laboratory parameters were not significantly correlated with US (r = 0.11, r = 0.13; P > .05, respectively), whereas x-rays were associated with a mean diagnostic delay of 2 ± 0.5 weeks (P < .05) compared with US. All 21 sonographic NHO patients survived to intensive care unit discharge. Twelve months after admission, 3/21 had died; 6/21 still had abnormalities by x-ray, US, and clinical examination; and 12/21 had resolution by x-ray, US, and clinical examination., Conclusion: Our pilot study suggests potential benefits for bedside US in NHO, especially when compared with x-ray or laboratory investigations. These include the possibility of earlier diagnosis, consideration of therapy before ossification, and ease of follow-up. Our study also provides insights into the morphologic, sonographic, radiologic, and clinical course of NHO.

Gastrointestinal ultrasound is a practical, safe, cheap and reproducible diagnostic tool in inflammatory bowel disease gaining global prominence amongst clinicians. Understanding the embryological processes of the intestinal tract assists in the interpretation of abnormal sonographic findings. In general terms, the examination principally comprises interrogation of the colon, mesentery and small intestine using both low-frequency and high-frequency probes. Interpretation of findings on GIUS includes assessment of bowel wall thickness, symmetry of this thickness, evidence of transmural changes, assessment of vascularity using Doppler imaging and assessment of other specific features including lymph nodes, mesentery and luminal motility. In addition to B-mode imaging, transperineal ultrasonography, elastography and contrast-enhanced ultrasonography are useful adjuncts. This supplement expands upon these features in more depth., Competing Interests: Conflict-of-interest statement: No potential conflicts of interest. No financial support.

Transcranial color-coded Doppler sonography is a noninvasive bedside ultrasound application that combines both imaging of parenchymal structures and Doppler assessment of intracranial vessels. It may aid in rapid diagnoses and treatment decision making of patients with intracranial emergencies in point-of-care settings. This pictorial essay illustrates the technical aspects and emergency department applications of transcranial color-coded Doppler sonography, and provides some rationale for implementation of this technique into the emergency department practice., (© 2017 by the American Institute of Ultrasound in Medicine.)

Over the last decade, the use of portable ultrasound scanners has enhanced the concept of point of care ultrasound (PoC-US), namely, "ultrasound performed at the bedside and interpreted directly by the treating clinician." PoC-US is not a replacement for comprehensive ultrasound, but rather allows physicians immediate access to clinical imaging for rapid and direct solutions. PoC-US has already revolutionized everyday clinical practice, and it is believed that it will dramatically change how ultrasound is applied in daily practice. However, its use and teaching are different from continent to continent and from country to country. This World Federation for Ultrasound in Medicine and Biology position paper discusses the current status and future perspectives of PoC-US. Particular attention is given to the different uses of PoC-US and its clinical significance, including within emergency and critical care medicine, cardiology, anesthesiology, rheumatology, obstetrics, neonatology, gynecology, gastroenterology and many other applications. In the future, PoC-US will be more diverse than ever and be included in medical student training., (Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

The science of lung ultrasound has grown tremendously over the last two decades and lung ultrasound has not only entered the mainstream of point of care ultrasound but has become a dominant topic. Understanding lung ultrasound signs and artifacts is critical to being able to correlate findings with actual pathology and normal anatomy and physiology. Investigators have described multiple lung ultrasound artifacts and findings and it is important to understand both the physics and anatomic basis behind them. Additionally, ultrasound machine use and transducer selection can significantly affect results obtained on patient during an examination and the provider must carefully choose the correct settings. This manuscript describes the state of the art in ultrasound artifact recognition and correlation as well as management of ultrasound technology to optimize diagnostic success.

Gastrointestinal ultrasound (GIUS) is an ultrasound application that has been practiced for more than 30 years. Recently, GIUS has enjoyed a resurgence of interest, and there is now strong evidence of its utility and accuracy as a diagnostic tool for multiple indications. The method of learning GIUS is not standardised and may incorporate mentorship, didactic teaching and e-learning. Simulation, using either low- or high-fidelity models, can also play a key role in practicing and honing novice GIUS skills. A course for training as well as establishing and evaluating competency in GIUS is proposed in the manuscript, based on established learning theory practice. We describe the broad utility of GIUS in clinical medicine, including a review of the literature and existing meta-analyses. Further, the manuscript calls for agreement on international standards regarding education, training and indications., (Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Background: Optic nerve sheath diameter (ONSD) measurement using ultrasound has been proposed as a rapid, non-invasive, point of care technique to estimate intra-cranial pressure (ICP). Ultrasonic measurement of the optic nerve sheath can be quite challenging and there is limited literature surrounding learning curves for this technique. We attempted to develop a method to estimate the reliability learning curve for ONSD measurement utilizing a unique definition of reliability: a plateau in within-subject variability with unchanged between-subject variability., Methods: As part of a previously published study, a single operator measured the ONSD in 120 healthy volunteers over a 6-month period. Utilizing the assumption that the four measurements made on each subject during this study should be equal, the relationship of within-subject variance was described using a quadratic-plateau model as assessed by segmental polynomial (knot) regression., Results: Segmental polynomial (knot) regression revealed a plateau in within-subject variance after the 21st subject. However, there was no difference in overall mean values [3.69 vs 3.68 mm (p = 0.884)] or between-subject variance [14.49 vs 11.92 (p = 0.54)] above or below this cutoff., Conclusions: This study suggests a significant finite learning curve associated with ONSD measurements. It also offers a unique method of calculating the learning curve associated with ONSD measurement.