Enhanced Point-of-Care Ultrasound Applications by Integrating Automated Feature-Learning Systems Using Deep Learning

The Promise of Artificial Intelligence-Assisted Point-of-Care Ultrasonography in Perioperative Care

The role of point-of-care ultrasonography in the perioperative setting has expanded rapidly over recent years. Revolutionizing this technology further is integrating artificial intelligence to assist clinicians in optimizing images, identifying anomalies, performing automated measurements and calculations, and facilitating diagnoses. Artificial intelligence can increase point-of-care ultrasonography efficiency and accuracy, making it an even more valuable point-of-care tool. Given this topic's importance and ever-changing landscape, this review discusses the latest trends to serve as an introduction and update in this area.

Monitoring homeostasis with ultrasound

An implant could allow at-home monitoring of deep-tissue changes after surgery.

Artificial intelligence evaluation of focused assessment with sonography in trauma

BACKGROUND

The focused assessment with sonography in trauma (FAST) is a widely used imaging modality to identify the location of life-threatening hemorrhage in a hemodynamically unstable trauma patient. This study evaluates the role of artificial intelligence in interpretation of the FAST examination abdominal views, as it pertains to adequacy of the view and accuracy of fluid survey positivity.

METHODS

Focused assessment with sonography for trauma examination images from 2015 to 2022, from trauma activations, were acquired from a quaternary care level 1 trauma center with more than 3,500 adult trauma evaluations, annually. Images pertaining to the right upper quadrant and left upper quadrant views were obtained and read by a surgeon or radiologist. Positivity was defined as fluid present in the hepatorenal or splenorenal fossa, while adequacy was defined by the presence of both the liver and kidney or the spleen and kidney for the right upper quadrant or left upper quadrant views, respectively. Four convolutional neural network architecture models (DenseNet121, InceptionV3, ResNet50, Vgg11bn) were evaluated.

RESULTS

A total of 6,608 images, representing 109 cases were included for analysis within the "adequate" and "positive" data sets. The models relayed 88.7% accuracy, 83.3% sensitivity, and 93.6% specificity for the adequate test cohort, while the positive cohort conferred 98.0% accuracy, 89.6% sensitivity, and 100.0% specificity against similar models. Augmentation improved the accuracy and sensitivity of the positive models to 95.1% accurate and 94.0% sensitive. DenseNet121 demonstrated the best accuracy across tasks.

CONCLUSION

Artificial intelligence can detect positivity and adequacy of FAST examinations with 94% and 97% accuracy, aiding in the standardization of care delivery with minimal expert clinician input. Artificial intelligence is a feasible modality to improve patient care imaging interpretation accuracy and should be pursued as a point-of-care clinical decision-making tool.

LEVEL OF EVIDENCE

Diagnostic Test/Criteria; Level III.

Current and future utility of ultrasound imaging in upper extremity musculoskeletal rehabilitation: A scoping review

STUDY DESIGN

This study was a scoping review.

BACKGROUND

Continued advances in musculoskeletal sonography technology and access have increased the feasibility of point-of-care use to support day-to-day clinical care and decision-making. Sonography can help improve therapeutic outcomes in upper extremity (UE) rehabilitation by enabling clinicians to visualize underlying structures during treatment.

PURPOSE OF THE STUDY

This study aimed to (1) evaluate the growth, range, extent, and composition of sonography literature supporting UE rehabilitation; (2) identify trends, gaps, and opportunities with regard to anatomic areas and diagnoses examined and ultrasound techniques used; and (3) evaluate potential research and practice utility.

METHODS

Searches were completed in PubMed, CINAHL, SPORTDiscus, PsycINFO, and BIOSIS. We included data-driven articles using ultrasound imaging for upper extremity structures in rehabilitation-related conditions. Articles directly applicable to UE rehabilitation were labeled direct articles, while those requiring translation were labeled indirect articles. Articles were further categorized by ultrasound imaging purpose. Article content between the two groups was descriptively compared, and direct articles underwent an evaluation of evidence levels and narrative synthesis to explore potential clinical utility.

RESULTS

Average publication rates for the final included articles (n = 337) steadily increased. Indirect articles (n = 288) used sonography to explore condition etiology, assess measurement properties, inform medical procedure choice, and grade condition severity. Direct articles (n = 49) used sonography to assess outcomes, inform clinical reasoning, and aid intervention delivery. Acute UE conditions and emerging sonography technology were rarely examined, while tendon, muscle, and soft tissue conditions and grayscale imaging were common. Rheumatic and peripheral nerve conditions and Doppler imaging were more prevalent in indirect than direct articles. Among reported sonography service providers, there was a high proportion of nonradiologist clinicians.

CONCLUSION

Sonography literature for UE rehabilitation demonstrates potential utility in evaluating outcomes, informing clinical reasoning, and assisting intervention delivery. A large peripheral knowledge base provides opportunities for clinical applications; however, further research is needed to determine clinical efficacy and impact for specific applications.

Use of Automated Machine Learning for Classifying Hemoperitoneum on Ultrasonographic Images of Morrison's Pouch: A Multicenter Retrospective Study

This study evaluated automated machine learning (AutoML) in classifying the presence or absence of hemoperitoneum in ultrasonography (USG) images of Morrison's pouch. In this multicenter, retrospective study, 864 trauma patients from trauma and emergency medical centers in South Korea were included. In all, 2200 USG images (1100 hemoperitoneum and 1100 normal) were collected. Of these, 1800 images were used for training and 200 were used for the internal validation of AutoML. External validation was performed using 100 hemoperitoneum images and 100 normal images collected separately from a trauma center that were not included in the training and internal validation sets. Google's open-source AutoML was used to train the algorithm in classifying hemoperitoneum in USG images, followed by internal and external validation. In the internal validation, the sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve were 95%, 99%, and 0.97, respectively. In the external validation, the sensitivity, specificity, and AUROC were 94%, 99%, and 0.97, respectively. The performances of AutoML in the internal and external validation were not statistically different (p = 0.78). A publicly available, general-purpose AutoML can accurately classify the presence or absence of hemoperitoneum in USG images of the Morrison's pouch of real-world trauma patients.

Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia

BACKGROUND

Ultrasound-guided regional anaesthesia relies on the visualisation of key landmark, target, and safety structures on ultrasound. However, this can be challenging, particularly for inexperienced practitioners. Artificial intelligence (AI) is increasingly being applied to medical image interpretation, including ultrasound. In this exploratory study, we evaluated ultrasound scanning performance by non-experts in ultrasound-guided regional anaesthesia, with and without the use of an assistive AI device.

METHODS

Twenty-one anaesthetists, all non-experts in ultrasound-guided regional anaesthesia, underwent a standardised teaching session in ultrasound scanning for six peripheral nerve blocks. All then performed a scan for each block; half of the scans were performed with AI assistance and half without. Experts assessed acquisition of the correct block view and correct identification of sono-anatomical structures on each view. Participants reported scan confidence, experts provided a global rating score of scan performance, and scans were timed.

RESULTS

Experts assessed 126 ultrasound scans. Participants acquired the correct block view in 56/62 (90.3%) scans with the device compared with 47/62 (75.1%) without (P=0.031, two data points lost). Correct identification of sono-anatomical structures on the view was 188/212 (88.8%) with the device compared with 161/208 (77.4%) without (P=0.002). There was no significant overall difference in participant confidence, expert global performance score, or scan time.

CONCLUSIONS

Use of an assistive AI device was associated with improved ultrasound image acquisition and interpretation. Such technology holds potential to augment performance of ultrasound scanning for regional anaesthesia by non-experts, potentially expanding patient access to these techniques.

CLINICAL TRIAL REGISTRATION

NCT05156099.

Deep learning-based important weights-only transfer learning approach for COVID-19 CT-scan classification

COVID-19 has become a pandemic for the entire world, and it has significantly affected the world economy. The importance of early detection and treatment of the infection cannot be overstated. The traditional diagnosis techniques take more time in detecting the infection. Although, numerous deep learning-based automated solutions have recently been developed in this regard, nevertheless, the limitation of computational and battery power in resource-constrained devices makes it difficult to deploy trained models for real-time inference. In this paper, to detect the presence of COVID-19 in CT-scan images, an important weights-only transfer learning method has been proposed for devices with limited runt-time resources. In the proposed method, the pre-trained models are made point-of-care devices friendly by pruning less important weight parameters of the model. The experiments were performed on two popular VGG16 and ResNet34 models and the empirical results showed that pruned ResNet34 model achieved 95.47% accuracy, 0.9216 sensitivity, 0.9567 F-score, and 0.9942 specificity with 41.96% fewer FLOPs and 20.64% fewer weight parameters on the SARS-CoV-2 CT-scan dataset. The results of our experiments showed that the proposed method significantly reduces the run-time resource requirements of the computationally intensive models and makes them ready to be utilized on the point-of-care devices.

Artificial Intelligence-Augmented Pediatric Lung POCUS: A Pilot Study of Novice Learners

OBJECTIVE

Respiratory symptoms are among the most common chief complaints of pediatric patients in the emergency department (ED). Point-of-care ultrasound (POCUS) outperforms conventional chest X-ray and is user-dependent, which can be challenging to novice ultrasound (US) users. We introduce a novel concept using artificial intelligence (AI)-enhanced pleural sweep to generate complete panoramic views of the lungs, and then assess its accuracy among novice learners (NLs) to identify pneumonia.

METHODS

Previously healthy 0- to 17-year-old patients presenting to a pediatric ED with cardiopulmonary chief complaint were recruited. NLs received a 1-hour training on traditional lung POCUS and the AI-assisted software. Two POCUS-trained experts interpreted the images, which served as the criterion standard. Both expert and learner groups were blinded to each other's interpretation, patient data, and outcomes. Kappa was used to determine agreement between POCUS expert interpretations.

RESULTS

Seven NLs, with limited to no prior POCUS experience, completed examinations on 32 patients. The average patient age was 5.53 years (±1.07). The median scan time of 7 minutes (minimum-maximum 3-43; interquartile 8). Three (8.8%) patients were diagnosed with pneumonia by criterion standard. Sensitivity, specificity, and accuracy for NLs AI-augmented interpretation were 66.7% (confidence interval [CI] 9.4-99.1%), 96.5% (CI 82.2-99.9%), and 93.7% (CI 79.1-99.2%). The average image quality rating was 2.94 (±0.16) out of 5 across all lung fields. Interrater reliability between expert sonographers was high with a kappa coefficient of 0.8.

CONCLUSION

This study shows that AI-augmented lung US for diagnosing pneumonia has the potential to increase accuracy and efficiency.

Medical ultrasound image speckle reduction and resolution enhancement using texture compensated multi-resolution convolution neural network

Ultrasound (US) imaging is a mature technology that has widespread applications especially in the healthcare sector. Despite its widespread use and popularity, it has an inherent disadvantage that ultrasound images are prone to speckle and other kinds of noise. The image quality in the low-cost ultrasound imaging systems is degraded due to the presence of such noise and low resolution of such ultrasound systems. Herein, we propose a method for image enhancement where, the overall quality of the US images is improved by simultaneous enhancement of US image resolution and noise suppression. To avoid over-smoothing and preserving structural/texture information, we devise texture compensation in our proposed method to retain the useful anatomical features. Moreover, we also utilize US image formation physics knowledge to generate augmentation datasets which can improve the training of our proposed method. Our experimental results showcase the performance of the proposed network as well as the effectiveness of the utilization of US physics knowledge to generate augmentation datasets.

Interobserver Agreement and Correlation of an Automated Algorithm for B-Line Identification and Quantification With Expert Sonologist Review in a Handheld Ultrasound Device

OBJECTIVES

B-lines are ultrasound artifacts that can be used to detect a variety of pathologic lung conditions. Computer-aided methods to detect and quantify B-lines may standardize quantification and improve diagnosis by novice users. We sought to test the performance of an automated algorithm for the detection and quantification of B-lines in a handheld ultrasound device (HHUD).

METHODS

Ultrasound images were prospectively collected on adult emergency department patients with dyspnea. Images from the first 124 patients were used for algorithm development. Clips from 80 unique subjects for testing were randomly selected in a predefined proportion of B-lines (0 B-lines, 1-2 B-lines, 3 or more B-lines) and blindly reviewed by five experts using both a manual and reviewer-adjusted process. Intraclass correlation coefficient (ICC) and weighted kappa were used to measure agreement, while an a priori threshold of an ICC (3,k) of 0.75 and precision of 0.3 were used to define adequate performance.

RESULTS

ICC between the algorithm and manual count was 0.84 (95% confidence interval [CI] 0.75-0.90), with a precision of 0.15. ICC between the reviewer-adjusted count and the algorithm count was 0.94 (95% CI 0.90-0.96), and the ICC between the manual and reviewer-adjusted counts was 0.94 (95% CI 0.90-0.96). Weighted kappa was 0.72 (95% CI 0.49-0.95), 0.88 (95% CI 0.74-1), and 0.85 (95% CI 0.89-0.96), respectively.

CONCLUSIONS

This study demonstrates a high correlation between point-of-care ultrasound experts and an automated algorithm to identify and quantify B-lines using an HHUD. Future research may incorporate this HHUD in clinical studies in multiple settings and users of varying experience levels.

Artificial intelligence (AI) versus expert: A comparison of left ventricular outflow tract velocity time integral (LVOT-VTI) assessment between ICU doctors and an AI tool

Purpose

The application of point of care ultrasound (PoCUS) in medical education is a relatively new course. There are still great differences in the existence, quantity, provision, and depth of bedside ultrasound education. The left ventricular outflow tract velocity time integral (LVOT‐VTI) has been successfully used in several studies as a parameter for hemodynamic management of critically ill patients, especially in the evaluation of fluid responsiveness. While LVOT‐VTI has been broadly used, valuable applications using artificial intelligence (AI) in PoCUS is still limited. We aimed to identify the degree of correlation between auto LVOT‐VTI and the manual LVOT‐VTI acquired by PoCUS trained ICU doctors.

Methods

Among the 58 ICU doctors who attended PoCUS training from 1 September 2019 to 30 November 2020, 46 ICU doctors who trained for more than 3 months were enrolled. At the end of PoCUS training, each of the enrolled ICU doctors acquired echocardiography parameters of a new ICU patient in 2 h after new patient was admitted. One of the two bedside expert sonographers would take standard echocardiogram of new ICU patients within 24 h. For ICU doctors, manual LVOT‐VTI was obtained for reference and auto LVOT‐VTI was calculated instantly by using an AI software tool. Based on the image quality of the auto LVOT‐VTI, ICU patients was separated into ideal group (n = 31) and average group (n = 15).

Results

Left ventricular end‐diastolic dimension (LVEDd, p = 0.1028), left ventricular ejection fraction (LVEF, p = 0.3251), left atrial dimension (LA‐d, p = 0.0962), left ventricular E/A ratio (p = 0.160), left ventricular wall motion (p = 0.317) and pericardial effusion (p = 1) had no significant difference between trained ICU doctors and expert sonographer. ICU patients in average group had greater sequential organ failure assessment (SOFA) score (7.33 ± 1.58 vs. 4.09 ± 0.57, p = 0.022) and lactic acid (3.67 ± 0.86 mmol/L vs. 1.46 ± 0.12 mmol/L, p = 0.0009) with greater value of LVEDd (51.93 ± 1.07 vs. 47.57 ± 0.89, p = 0.0053), LA‐d (39.06 ± 1.47 vs. 35.22 ± 0.98, p = 0.0334) and percentage of decreased wall motion (p = 0.0166) than ideal group. There were no significant differences of δLVOT‐VTI (|manual LVOT‐VTI – auto LVOT‐VTI|/manual VTI*100%) between the two groups (8.8% ± 1.3% vs. 10% ± 2%, p = 0.6517). Statistically, significant correlations between manual LVOT‐VTI and auto LVOT‐VTI were present in the ideal group (R² = 0.815, p = 0.00) and average group (R² = 0.741, p = 0.00).

Conclusions

ICU doctors could achieve the satisfied level of expertise as expert sonographers after 3 months of PoCUS training. Nearly two thirds of the enrolled ICU doctors could obtain the ideal view and one third of them could acquire the average view. ICU patients with higher SOFA scores and lactic acid were less likely to acquire the ideal view. Manual and auto LVOT‐VTI had statistically significant agreement in both ideal and average groups. Auto LVOT‐VTI in ideal view was more relevant with the manual LVOT‐VTI than the average view. AI might provide real‐time guidance among novice operators who lack expertise to acquire the ideal standard view.

The Evolving Importance of Artificial Intelligence and Radiology in Medical Trainee Education

Radiology education is understood to be an important component of medical school and resident training, yet lacks a standardization of instruction. The lack of uniformity in both how radiology is taught and learned has afforded opportunities for new technologies to intervene. Now with the integration of artificial intelligence within medicine, it is likely that the current medical trainee curricula will experience the impact it has to offer both for education and medical practice. In this paper, we seek to investigate the landscape of radiologic education within the current medical trainee curricula, and also to understand how artificial intelligence may potentially impact the current and future radiologic education model.

Advanced Ultrasound and Photoacoustic Imaging in Cardiology

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide. An effective management and treatment of CVDs highly relies on accurate diagnosis of the disease. As the most common imaging technique for clinical diagnosis of the CVDs, US imaging has been intensively explored. Especially with the introduction of deep learning (DL) techniques, US imaging has advanced tremendously in recent years. Photoacoustic imaging (PAI) is one of the most promising new imaging methods in addition to the existing clinical imaging methods. It can characterize different tissue compositions based on optical absorption contrast and thus can assess the functionality of the tissue. This paper reviews some major technological developments in both US (combined with deep learning techniques) and PA imaging in the application of diagnosis of CVDs.

Discriminative deep learning based benignity/malignancy diagnosis of dermatologic ultrasound skin lesions with pretrained artificial intelligence architecture

BACKGROUND

Deep-learning algorithms (DLAs) have been used in artificial intelligence aided ultrasonography diagnosis of thyroid and breast lesions. However, its use has not been described in the case of dermatologic ultrasound lesions. Our purpose was to train a DLA to discriminate benign form malignant lesions in dermatologic ultrasound images.

MATERIALS AND METHODS

We trained a prebuilt neural network architecture (EfficientNet B4) in a commercial artificial intelligence platform (Peltarion, Stockholm, Sweden) with 235 color Doppler images of both benign and malignant ultrasound images of 235 excised and histologically confirmed skin lesions (84.3% training, 15.7% validation). An additional 35 test images were used for testing the algorithm discrimination for correct benign/malignant diagnosis. One dermatologist with more than 5 years of experience in dermatologic ultrasound blindly evaluated the same 35 test images for malignancy or benignity.

RESULTS

EfficientNet B4 trained dermatologic ultrasound algorithm sensitivity; specificity; predictive positive values, and predicted negative values for validation algorithm were 0.8, 0.86, 0.86, and 0.8, respectively for malignancy diagnosis. When tested with 35 previously unevaluated images sets, the algorithm´s accuracy for correct benign/malignant diagnosis was 77.1%, not statistically significantly different from the dermatologist's evaluation (74.1%).

CONCLUSION

An adequately trained algorithm, even with a limited number of images, is at least as accurate as a dermatologic-ultrasound experienced dermatologist in the evaluation of benignity/malignancy of ultrasound skin tumor images devoid of clinical data.

Development of a Deep Learning Network to Classify Inferior Vena Cava Collapse to Predict Fluid Responsiveness

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.

The Use of Handheld Ultrasound Devices in Emergency Medicine

Purpose of Review

Ultraportable handheld ultrasound (HHU) devices are being rapidly adopted by emergency medicine (EM) physicians. Though knowledge of the breadth of their utility and functionality is still limited compared to cart-based systems, these machines are becoming more common due to ease-of-use, extreme affordability, and improving technology.

Recent Findings

Images obtained with HHU are comparable to those obtained with traditional machines but create unique issues regarding billing and data management. HHU devices are increasingly used successfully to augment the education of practitioners-in-training, by emergency physicians in austere environments, and in the burgeoning fields of "tele-ultrasound" and augmented reality scanning.

Summary

This review seeks to describe the current state of use of HHU devices in the emergency department (ED) including device overview, institutional concerns, unique areas of use, recent literature since their adoption into clinical EM, and their future potential.

Deep Learning in Kidney Ultrasound: Overview, Frontiers, and Challenges

Ultrasonography is a practical imaging technique used in numerous health care settings. It is relatively inexpensive, portable, and safe, and it has dynamic capabilities that make it an invaluable tool for a wide variety of diagnostic and interventional studies. Recently, there has been a revolution in medical imaging using artificial intelligence (AI). A particularly potent form of AI is deep learning, in which the computer learns to recognize pixel or written data on its own without the selection of predetermined features, usually through a specific neural network architecture. Neural networks vary in architecture depending on their task, and key design considerations include the number of layers and complexity, data available, technical requirements, and domain knowledge. Deep learning models offer the potential for promising innovations to workflow, image quality, and vision tasks in sonography. However, there are key limitations and challenges in creating reliable and safe AI models for patients and clinicians.

There is No Substitute for Human Intelligence

How to cite this article: Kumar V. There is No Substitute for Human Intelligence. Indian J Crit Care Med 2021;25(5):486-488.

AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound

Hepatocellular carcinoma (HCC) is a common cancer worldwide. Recent international guidelines request an identification of the stage and patient background/condition for an appropriate decision for the management direction. Radiomics is a technology based on the quantitative extraction of image characteristics from radiological imaging modalities. Artificial intelligence (AI) algorithms are the principal axis of the radiomics procedure and may provide various results from large data sets beyond conventional techniques. This review article focused on the application of the radiomics-related diagnosis of HCC using radiological imaging (computed tomography, magnetic resonance imaging, and ultrasound (B-mode, contrast-enhanced ultrasound, and elastography)), and discussed the current role, limitation and future of ultrasound. Although the evidence has shown the positive effect of AI-based ultrasound in the prediction of tumor characteristics and malignant potential, posttreatment response and prognosis, there are still a number of issues in the practical management of patients with HCC. It is highly expected that the wide range of applications of AI for ultrasound will support the further improvement of the diagnostic ability of HCC and provide a great benefit to the patients.

Rural emergency departments: A systematic review to develop a resource typology relevant to developed countries

OBJECTIVE

Despite low patient numbers, rural emergency departments have a similar diversity of case presentations as urban tertiary hospitals, with the need to manage high-acuity cases with limited resources. There are no consistent descriptions of the resources available to rural emergency departments internationally, limiting the capacity to compare clinical protocols and standards of care across similarly resourced units. This review aimed to describe the range of human, physical and specialist resources described in rural emergency departments in developed countries and propose a typology for use internationally.

DESIGN AND SETTING

A systematic literature search was performed for journal articles between 2000 and 2019 describing the staffing, access to radiology and laboratory investigations, and hospital inpatient specialists.

RESULTS

Considerable diversity in defining rurality and in resource access was found within and between Australia, New Zealand, Canada and USA.

DISCUSSION

A typology was developed to account for (a) emergency department staff on-floor, (b) emergency department staff on-call, (c) physical resources and (d) access to a specialist surgical service. This provides a valuable tool for relevant stakeholders to effectively communicate rural emergency department resources within a country and internationally.

CONCLUSION

The proposed five-tiered typology draws together international literature regarding rural emergency department services. Although further research is required to test this tool, the formation of this common language allows a base for effective communication between governments, training providers and policy-makers who are seeking to improve health systems and health outcomes.

Lung ultrasound for early diagnosis and severity assessment of pneumonia in patients with coronavirus disease 2019

BACKGROUND/AIMS

Current evidence supports lung ultrasound as a point-ofcare alternative diagnostic tool for various respiratory diseases. We sought to determine the utility of lung ultrasound for early detection of pneumonia and for assessment of respiratory failure among patients with coronavirus disease 2019 (COVID-19).

METHODS

Six patients with confirmed COVID-19 by reverse transcription-polymerase chain reaction were enrolled. All had undergone chest X-ray and chest computed tomography (CT) on the day of admission and underwent multiple point-of-care lung ultrasound scans over the course of their hospitalization.

RESULTS

Lung ultrasound detected early abnormal findings of representative B-lines in a patient with a normal chest X-ray, corresponding to ground-glass opacities on the chest CT scan. The ultrasound findings improved as her clinical condition improved and her viral load decreased. In another minimally symptomatic patient without significant chest X-ray findings, the ultrasound showed B-lines, an early sign of pneumonia before abnormalities were detected on the chest CT scan. In two critically ill patients, ultrasound was performed to assess for evaluation of disease severity. In both patients, the clinicians conducted emergency rapid sequence intubation based on the ultrasound findings without awaiting the laboratory results and radiological reports. In two children, ultrasound was used to assess the improvement in their pneumonia, thus avoiding further imaging tests such as chest CT.

CONCLUSION

Lung ultrasound is feasible and useful as a rapid, sensitive, and affordable point-of-care screening tool to detect pneumonia and assess the severity of respiratory failure in patients hospitalized with COVID-19.

Perioperative Point-of-Care Ultrasound

This clinical focus review targets all anesthesiologists and seeks to highlight the following aspects of perioperative point-of-care ultrasound: clinical utility, technology advancements, training/certification, education, reporting/billing, and limitations.

Stakeholder Perceptions of Point-of-Care Ultrasound Implementation in Resource-Limited Settings

Background: Nearly half of the world lacks access to diagnostic imaging. Point of care ultrasound (POCUS) is a versatile and relatively affordable imaging modality that offers promise as a means of bridging the radiology gap and improving care in low resource settings. Methods: We performed semi-structured interviews of key stakeholders at two diverse hospitals where POCUS implementation programs had recently been conducted: one in a rural private hospital in Haiti and the other in a public referral hospital in Malawi. Questions regarding the clinical utility of POCUS, as well as barriers and facilitators of its implementation, were asked of study participants. Using the Framework Method, analysis of interview transcripts was guided by the WHO ASSURED criteria for point of care diagnostics. Results: Fifteen stakeholders with diverse roles in POCUS implementation were interviewed. Interviewees from both sites considered POCUS a valuable diagnostic tool that improved clinical decisions. They perceived barriers to adequate training as one of the most important remaining barriers to POCUS implementation. Conclusions: In spite of the increasing affordability and portability of ultrasounds devices, there are still important barriers to the implementation of POCUS in resource-limited settings.