A Review of Early Experience in Lung Ultrasound in the Diagnosis and Management of COVID-19

Knowledge fused latent representation from lung ultrasound examination for COVID-19 pneumonia severity assessment

COVID-19 pneumonia severity assessment is of great clinical importance, and lung ultrasound (LUS) plays a crucial role in aiding the severity assessment of COVID-19 pneumonia due to its safety and portability. However, its reliance on qualitative and subjective observations by clinicians is a limitation. Moreover, LUS images often exhibit significant heterogeneity, emphasizing the need for more quantitative assessment methods. In this paper, we propose a knowledge fused latent representation framework tailored for COVID-19 pneumonia severity assessment using LUS examinations. The framework transforms the LUS examination into latent representation and extracts knowledge from regions labeled by clinicians to improve accuracy. To fuse the knowledge into the latent representation, we employ a knowledge fusion with latent representation (KFLR) model. This model significantly reduces errors compared to approaches that lack prior knowledge integration. Experimental results demonstrate the effectiveness of our method, achieving high accuracy of 96.4 % and 87.4 % for binary-level and four-level COVID-19 pneumonia severity assessments, respectively. It is worth noting that only a limited number of studies have reported accuracy for clinically valuable exam level assessments, and our method surpass existing methods in this context. These findings highlight the potential of the proposed framework for monitoring disease progression and patient stratification in COVID-19 pneumonia cases.

Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound

With the 2019 coronavirus disease (COVID-19) pandemic, there is an increasing demand for remote monitoring technologies to reduce patient and provider exposure. One field that has an increasing potential is teleguided ultrasound, where telemedicine and point-of-care ultrasound (POCUS) merge to create this new scope. Teleguided POCUS can minimize staff exposure while preserving patient safety and oversight during bedside procedures. In this paper, we propose the use of teleguided POCUS supported by AI technologies for the remote monitoring of COVID-19 patients by non-experienced personnel including self-monitoring by the patients themselves. Our hypothesis is that AI technologies can facilitate the remote monitoring of COVID-19 patients through the utilization of POCUS devices, even when operated by individuals without formal medical training. In pursuit of this goal, we performed a pilot analysis to evaluate the performance of users with different clinical backgrounds using a computer-based system for COVID-19 detection using lung ultrasound. The purpose of the analysis was to emphasize the potential of the proposed AI technology for improving diagnostic performance, especially for users with less experience.

Automated Lung Ultrasound Pulmonary Disease Quantification Using an Unsupervised Machine Learning Technique for COVID-19

COVID-19 is an ongoing global health pandemic. Although COVID-19 can be diagnosed with various tests such as PCR, these tests do not establish pulmonary disease burden. Whereas point-of-care lung ultrasound (POCUS) can directly assess the severity of characteristic pulmonary findings of COVID-19, the advantage of using US is that it is inexpensive, portable, and widely available for use in many clinical settings. For automated assessment of pulmonary findings, we have developed an unsupervised learning technique termed the calculated lung ultrasound (CLU) index. The CLU can quantify various types of lung findings, such as A or B lines, consolidations, and pleural effusions, and it uses these findings to calculate a CLU index score, which is a quantitative measure of pulmonary disease burden. This is accomplished using an unsupervised, patient-specific approach that does not require training on a large dataset. The CLU was tested on 52 lung ultrasound examinations from several institutions. CLU demonstrated excellent concordance with radiologist findings in different pulmonary disease states. Given the global nature of COVID-19, the CLU would be useful for sonographers and physicians in resource-strapped areas with limited ultrasound training and diagnostic capacities for more accurate assessment of pulmonary status.

Point-of-Care Ultrasound of Post-acute COVID-19 Syndrome: A Prospective Cohort Study

Introduction Acute COVID-19 patients can suffer from chronic symptoms known as post-acute sequelae of SARS-CoV-2 infection (PASC). Point-of-care ultrasound (POCUS) is established in acute COVID, but its utility in PASC is unclear. We sought to determine the incidence of cardiac and pulmonary abnormalities with POCUS in patients with PASC in a COVID-19 recovery clinic. Methods This prospective cohort study included adults (>18 years old) presenting with cardiopulmonary symptoms to the COVID-19 recovery clinic. A lung ultrasound and standard bedside echocardiogram were performed by ultrasound-trained physicians. Images were interpreted in real time by the performing sonographer and independently by a blinded ultrasound faculty member. Discrepancies in interpretation were addressed by consensus review. A modified Soldati score was calculated by the sum of the scores in each of the 12 lung zones, with each zone score ranging from 0 to 3 (maximum score of 36). The score was then compared to clinical outcomes and outpatient testing.  Results Between April and July 2021, 41 patients received POCUS examinations, with 24 of those included in the study. In all, 15 out of 24 (62.5%) had a normal lung ultrasound. Of the nine subjects with lung abnormalities, the median modified Soldati score was 2. Three patients had trivial pericardial effusions, and all had normal left and right ventricular size and function. Conclusion The majority (62.5%) of patients presenting to the PASC clinic had a normal pulmonary ultrasound, and the vast majority (87.5%) had normal cardiac ultrasounds. These findings suggest that cardiopulmonary symptoms in PASC may be from etiologies not well evaluated by POCUS.

Additive value of lung ultrasound to clinical parameters for prognosticating COVID-19

The inclusion of LUS with simple, point-of-care clinical parameters have potential to improve COVID-19 prognostication above that from standard clinical care delivery. https://bit.ly/3InePYK.

State of the Art in Lung Ultrasound, Shifting from Qualitative to Quantitative Analyses

Lung ultrasound (LUS) has been increasingly expanding since the 1990s, when the clinical relevance of vertical artifacts was first reported. However, the massive spread of LUS is only recent and is associated with the coronavirus disease 2019 (COVID-19) pandemic, during which semi-quantitative computer-aided techniques were proposed to automatically classify LUS data. In this review, we discuss the state of the art in LUS, from semi-quantitative image analysis approaches to quantitative techniques involving the analysis of radiofrequency data. We also discuss recent in vitro and in silico studies, as well as research on LUS safety. Finally, conclusions are drawn highlighting the potential future of LUS.

Lung ultrasound findings in hospitalized COVID-19 patients in relation to venous thromboembolic events: the ECHOVID-19 study

PURPOSE

Several studies have reported thromboembolic events to be common in severe COVID-19 cases. We sought to investigate the relationship between lung ultrasound (LUS) findings in hospitalized COVID-19 patients and the development of venous thromboembolic events (VTE).

METHODS

A total of 203 adults were included from a COVID-19 ward in this prospective multi-center study (mean age 68.6 years, 56.7% men). All patients underwent 8-zone LUS, and all ultrasound images were analyzed off-line blinded. Several LUS findings were investigated (total number of B-lines, B-line score, and LUS-scores).

RESULTS

Median time from admission to LUS examination was 4 days (IQR: 2, 8). The median number of B-lines was 12 (IQR: 8, 18), and 44 (21.7%) had a positive B-line score. During hospitalization, 17 patients developed VTE (4 deep-vein thrombosis, 15 pulmonary embolism), 12 following and 5 prior to LUS. In fully adjusted multivariable Cox models (excluding participants with VTE prior to LUS), all LUS parameters were significantly associated with VTE (total number of B-lines: HR = 1.14, 95% CI (1.03, 1.26) per 1 B-line increase), positive B-line score: HR = 9.79, 95% CI (1.87, 51.35), and LUS-score: HR = 1.51, 95% CI (1.10, 2.07), per 1-point increase). The B-line score and LUS-score remained significantly associated with VTE in sensitivity analyses.

CONCLUSION

In hospitalized COVID-19 patients, pathological LUS findings were common, and the total number of B-lines, B-line score, and LUS-score were all associated with VTE. These findings indicate that the LUS examination may be useful in risk stratification and the clinical management of COVID-19. These findings should be considered hypothesis generating.

CLINICALTRIALS

GOV ID

NCT04377035.

Artificial Intelligence for COVID-19 Detection in Medical Imaging-Diagnostic Measures and Wasting-A Systematic Umbrella Review

The COVID-19 pandemic has sparked a barrage of primary research and reviews. We investigated the publishing process, time and resource wasting, and assessed the methodological quality of the reviews on artificial intelligence techniques to diagnose COVID-19 in medical images. We searched nine databases from inception until 1 September 2020. Two independent reviewers did all steps of identification, extraction, and methodological credibility assessment of records. Out of 725 records, 22 reviews analysing 165 primary studies met the inclusion criteria. This review covers 174,277 participants in total, including 19,170 diagnosed with COVID-19. The methodological credibility of all eligible studies was rated as critically low: 95% of papers had significant flaws in reporting quality. On average, 7.24 (range: 0-45) new papers were included in each subsequent review, and 14% of studies did not include any new paper into consideration. Almost three-quarters of the studies included less than 10% of available studies. More than half of the reviews did not comment on the previously published reviews at all. Much wasting time and resources could be avoided if referring to previous reviews and following methodological guidelines. Such information chaos is alarming. It is high time to draw conclusions from what we experienced and prepare for future pandemics.

Egyptian Consensus on the Role of Lung Ultrasonography During the Coronavirus Disease 2019 Pandemic

Background & Aims

Coronavirus disease 2019 (COVID-19) is a global health problem, presenting with symptoms ranging from mild nonspecific symptoms to serious pneumonia. Early screening techniques are essential in the diagnosis and assessment of disease progression. This consensus was designed to clarify the role of lung ultrasonography versus other imaging modalities in the COVID-19 pandemic.

Methods

A multidisciplinary team consisting of experts from different specialties (ie, pulmonary diseases, infectious diseases, intensive care unit and emergency medicine, radiology, and public health) who deal with patients with COVID-19 from different geographical areas was classified into task groups to review the literatures from different databases and generate 10 statements. The final consensus statements were based on expert physically panelists’ discussion held in Cairo July 2021 followed by electric voting for each statement.

Results

The statements were electronically voted to be either “agree,” “not agree,” or “neutral.” For a statement to be accepted to the consensus, it should have 80% agreement.

Conclusion

Lung ultrasonography is a rapid and useful tool, which can be performed at bedside and overcomes computed tomography limitations, for screening and monitoring patients with COVID-19 with an accepted accuracy rate.

Lung ultrasound findings following COVID-19 hospitalization: A prospective longitudinal cohort study

Background

Lung ultrasound (LUS) is a useful tool for diagnosis and monitoring in patients with active COVID-19-infection. However, less is known about the changes in LUS findings after a hospitalization for COVID-19.

Methods

In a prospective, longitudinal study in patients with COVID-19 enrolled from non-ICU hospital units, adult patients underwent 8-zone LUS and blood sampling both during the hospitalization and 2–3 months after discharge. LUS images were analyzed blinded to clinical variables and outcomes.

Results

A total of 71 patients with interpretable LUS at baseline and follow up (mean age 64 years, 61% male, 24% with acute respiratory distress syndrome (ARDS)) were included. The follow-up LUS was performed a median of 72 days after the initial LUS performed during hospitalization. At baseline, 87% had pathologic LUS findings in ≥1 zone (e.g. ≥3 B-lines, confluent B-lines or subpleural or lobar consolidation), whereas 30% had pathologic findings at follow-up (p < 0.001). The total number of B-lines and LUS score decreased significantly from hospitalization to follow-up (median 17 vs. 4, p < 0.001 and 4 vs. 0, p < 0.001, respectively). On the follow-up LUS, 28% of all patients had ≥3 B-lines in ≥1 zone, whereas in those with ARDS during the baseline hospitalization (n = 17), 47% had ≥3 B-lines in ≥1 zone.

Conclusion

LUS findings improved significantly from hospitalization to follow-up 2–3 months after discharge in COVID-19 survivors. However, persistent B-lines were frequent at follow-up, especially among those who initially had ARDS. LUS seems to be a promising method to monitor COVID-19 lung changes over time.

Clinicaltrials.gov ID

NCT04377035.

Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic

Ultrasound imaging of the lung has played an important role in managing patients with COVID-19-associated pneumonia and acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic, lung ultrasound (LUS) or point-of-care ultrasound (POCUS) has been a popular diagnostic tool due to its unique imaging capability and logistical advantages over chest X-ray and CT. Pneumonia/ARDS is associated with the sonographic appearances of pleural line irregularities and B-line artefacts, which are caused by interstitial thickening and inflammation, and increase in number with severity. Artificial intelligence (AI), particularly machine learning, is increasingly used as a critical tool that assists clinicians in LUS image reading and COVID-19 decision making. We conducted a systematic review from academic databases (PubMed and Google Scholar) and preprints on arXiv or TechRxiv of the state-of-the-art machine learning technologies for LUS images in COVID-19 diagnosis. Openly accessible LUS datasets are listed. Various machine learning architectures have been employed to evaluate LUS and showed high performance. This paper will summarize the current development of AI for COVID-19 management and the outlook for emerging trends of combining AI-based LUS with robotics, telehealth, and other techniques.

Deep Learning and Medical Image Analysis for COVID-19 Diagnosis and Prediction

The coronavirus disease 2019 (COVID-19) pandemic has imposed dramatic challenges to health-care organizations worldwide. To combat the global crisis, the use of thoracic imaging has played a major role in diagnosis, prediction, and management for COVID-19 patients with moderate to severe symptoms or with evidence of worsening respiratory status. In response, the medical image analysis community acted quickly to develop and disseminate deep learning models and tools to meet the urgent need of managing and interpreting large amounts of COVID-19 imaging data. This review aims to not only summarize existing deep learning and medical image analysis methods but also offer in-depth discussions and recommendations for future investigations. We believe that the wide availability of high-quality, curated, and benchmarked COVID-19 imaging data sets offers the great promise of a transformative test bed to develop, validate, and disseminate novel deep learning methods in the frontiers of data science and artificial intelligence. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 24 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Lung Ultrasound Findings Associated With COVID-19 ARDS, ICU Admission, and All-Cause Mortality

BACKGROUND

As lung ultrasound (LUS) has emerged as a diagnostic tool in patients with COVID-19, we sought to investigate the association between LUS findings and the composite in-hospital outcome of ARDS incidence, ICU admission, and all-cause mortality.

METHODS

In this prospective, multi-center, observational study, adults with laboratory-confirmed SARS-CoV-2 infection were enrolled from non-ICU in-patient units. Subjects underwent an LUS evaluating a total of 8 zones. Images were analyzed off-line, blinded to clinical variables and outcomes. A LUS score was developed to integrate LUS findings: ≥ 3 B-lines corresponded to a score of 1, confluent B-lines to a score of 2, and subpleural or lobar consolidation to a score of 3. The total LUS score ranged from 0-24 per subject.

RESULTS

Among 215 enrolled subjects, 168 with LUS data and no current signs of ARDS or ICU admission (mean age 59 y, 56% male) were included. One hundred thirty-six (81%) subjects had pathologic LUS findings in ≥ 1 zone (≥ 3 B-lines, confluent B-lines, or consolidations). Markers of disease severity at baseline were higher in subjects with the composite outcome (n = 31, 18%), including higher median C-reactive protein (90 mg/L vs 55, P < .001) and procalcitonin levels (0.35 μg/L vs 0.13, P = .033) and higher supplemental oxygen requirements (median 4 L/min vs 2, P = .001). However, LUS findings and score did not differ significantly between subjects with the composite outcome and those without, and were not associated with outcomes in unadjusted and adjusted logistic regression analyses.

CONCLUSIONS

Pathologic findings on LUS were common a median of 3 d after admission in this cohort of non-ICU hospitalized subjects with COVID-19 and did not differ among subjects who experienced the composite outcome of incident ARDS, ICU admission, and all-cause mortality compared to subjects who did not. These findings should be confirmed in future investigations. The study is registered at Clinicaltrials.gov (NCT04377035).

Variability in Point-of-Care Lung Ultrasound Findings in Pediatric COVID-19 Patients: A Multicenter Case Series

ABSTRACT

Point-of-care ultrasound (POCUS) has been described as a useful tool for identification of coronavirus disease 2019 (COVID-19) in adults and children. Although several case reports describe POCUS findings in children with COVID-19, to our knowledge, there have been no published multicenter case series describing the large heterogeneity in lung POCUS findings in pediatric COVID-19. This series includes 7 symptomatic patients with COVID-19 who had a lung POCUS performed at 6 institutions by pediatric emergency attendings and fellows. The findings were variable, ranging from no findings to the appearance of B-lines, pleural abnormalities, consolidations, and a pleural effusion. Further studies are needed to improve our understanding, characterization, and prognostic correlation of POCUS findings in this novel disease in children.

Diagnostic imaging in COVID-19 pneumonia: a literature review

In December 2019 in Wuhan (China), a bat-origin coronavirus (2019-nCoV), also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified, and the World Health Organization named the related disease COVID-19. Its most severe manifestations are pneumonia, systemic and pulmonary thromboembolism, acute respiratory distress syndrome (ARDS), and respiratory failure. A swab test is considered the gold standard for the diagnosis of COVID-19 despite the high number of false negatives. Radiologists play a crucial role in the rapid identification and early diagnosis of pulmonary involvement. Lung ultrasound (LUS) and computed tomography (CT) have a high sensitivity in detecting pulmonary interstitial involvement. LUS is a low-cost and radiation-free method, which allows a bedside approach and needs disinfection of only a small contact area, so it could be particularly useful during triage and in intensive care units (ICUs). High-resolution computed tomography (HRCT) is particularly useful in evaluating disease progression or resolution, being able to identify even the smallest changes.

The Role and Challenges of Clinical Imaging During COVID-19 Outbreak

Objective:

The Radiology department played a crucial role in detecting and following up with the COVID-19 disease during the pandemic. The purpose of this review was to highlight and discuss the role of each imaging modality, in the radiology department, that can help in the current pandemic and to determine the challenges faced by staff and how to overcome them.

Materials and Methods:

A literature search was performed using different databases, including PubMed, Google scholar, and the college electronic library to access 2020 published related articles.

Results:

A chest computed tomogram (CT) was found to be superior to a chest radiograph, with regards to the early detection of COVID-19. Utilizing lung point of care ultrasound (POCUS) with pediatric patients, demonstrated excellent sensitivity and specificity, compared to a chest radiography. In addition, lung ultrasound (LUS) showed a high correlation with the disease severity assessed with CT. However, magnetic resonance imaging (MRI) has some limiting factors with regard to its clinical utilization, due to signal loss. The reported challenges that the radiology department faced were mainly related to infection control, staff workload, and the training of students.

Conclusion:

The choice of an imaging modality to provide a COVID-19 diagnosis is debatable. It depends on several factors that should be carefully considered, such as disease stage, mobility of the patient, and ease of applying infection control procedures. The pros and cons of each imaging modality were highlighted, as part of this review. To control the spread of the infection, precautionary measures such as the use of portable radiographic equipment and the use of personal protective equipment (PPE) must be implemented.

U SO CARE-The Impact of Cardiac Ultrasound during Cardiopulmonary Resuscitation: A Prospective Randomized Simulator-Based Trial

BACKGROUND

Actual cardiopulmonary resuscitation (CPR) guidelines recommend point-of-care ultrasound (POCUS); however, data on POCUS during CPR are sparse and conflicting. This randomized trial investigated the effects of POCUS during CPR on team performance and diagnostic accuracy.

METHODS

Intensive Care and Emergency Medicine residents performed CPR with or without available POCUS in simulated cardiac arrests. The primary endpoint was hands-on time. Data analysis was performed using video recordings.

RESULTS

Hands-on time was 89% (87-91) in the POCUS and 92% (89-94) in the control group (difference 3, 95% CI for difference 2-4, p < 0.001). POCUS teams had delayed defibrillator attachments (33 vs. 26 sec, p = 0.017) and first rhythm analysis (74 vs. 52 sec, p = 0.001). Available POCUS was used in 71%. Of the POCUS teams, 3 stated a POCUS-derived diagnosis, with 49 being correct and 42 followed by a correct treatment decision. Four teams made a wrong diagnosis and two made an inappropriate treatment decision.

CONCLUSIONS

POCUS during CPR resulted in lower hands-on times and delayed rhythm analysis. Correct POCUS diagnoses occurred in 52%, correct treatment decisions in 44%, and inappropriate treatment decisions in 2%. Training on POCUS during CPR should focus on diagnostic accuracy and maintenance of high-quality CPR.

Impulse Oscillometry Findings and Their Associations With Lung Ultrasound Signs in COVID-19 Survivors

BACKGROUND

Because impulse oscillometry (IOS) can detect changes in the small airways and is safer to perform during the COVID-19 pandemic than other pulmonary function tests, it may have value in investigating pulmonary sequelae in COVID-19 survivors. This study evaluated the performance of IOS in detecting lung abnormalities in COVID-19 survivors and investigated the associations of the findings with those of lung ultrasound (LUS) and spirometry.

METHODS

In this cross-sectional study, 117 subjects underwent IOS at a frequency range of 4-20 Hz 2 months after COVID-19 diagnosis. They also underwent spirometry and LUS, and their aeration scores were calculated.

RESULTS

On IOS, the resonance frequency was > 12 Hz, and the area under the reactance curve was > 3.60 cm H₂O/L/s in 70 (59.8%) and 55 (47.0%) subjects, respectively. A heterogeneity of resistance between R4 and R20 (R4-R20) > 20% was observed in 60 (51.3%) participants. Based on their abnormalities in resistive and reactive parameters, 76 (65.0%) participants had abnormal IOS. Spirometry abnormalities were detected in 40 (34.2%) cases. LUS was abnormal in 51 (43.6%) participants, and the median aeration score was 0 (0-8) points. Abnormal IOS was associated with abnormal LUS (P < .001) and abnormal spirometry (P = .002). Abnormal spirometry had a significant but weaker association with abnormal LUS (P = .031). In participants who reported hospitalization, abnormal IOS was associated with both abnormal LUS (P = .001) and abnormal spirometry (P = .006). In participants who did not report hospitalization, abnormal IOS was associated with abnormal LUS (P < .001) but not abnormal spirometry (P = .063).

CONCLUSIONS

In COVID-19 survivors, IOS detected changes even when spirometry was normal. In these individuals, IOS parameters were more strongly associated with abnormalities on LUS than with abnormalities on spirometry.

Modeling Pulsed High-Power Spikes in Tunable HV Capacitive Drivers of Piezoelectric Wideband Transducers to Improve Dynamic Range and SNR for Ultrasonic Imaging and NDE

The signal-to-noise ratios (SNR) of ultrasonic imaging and non-destructive evaluation (NDE) applications can be greatly improved by driving each piezoelectric transducer (single or in array) with tuned HV capacitive-discharge drivers. These can deliver spikes with kW pulsed power at PRF ≈ 5000 spikes/s, achieving levels higher even than in CW high-power ultrasound: up to 5 kWpp. These conclusions are reached here by applying a new strategy proposed for the accurate modeling of own-design re-configurable HV capacitive drivers. To obtain such rigorous spike modeling, the real effects of very high levels of pulsed intensities (3-10 A) and voltages (300-700 V) were computed. Unexpected phenomena were found: intense brief pulses of driving power and probe emitted force, as well as nonlinearities in semiconductors, though their catalog data include only linear ranges. Fortunately, our piezoelectric and circuital devices working in such an intense regime have not shown serious heating problems, since the finally consumed "average" power is rather small. Intensity, power, and voltage, driving wideband transducers from our capacitive drivers, are researched here in order to drastically improve (∆ >> 40 dB) their ultrasonic "net dynamic range available" (NDRA), achieving emitted forces > 240 Newtonspp and receiving ultrasonic signals of up to 76-205 Vpp. These measurements of ultrasonic pulsed voltages, received in NDE and Imaging, are approximately 10,000 larger than those usual today. Thus, NDRA ranges were optimized for three laboratory capacitive drivers (with six commercial transducers), which were successfully applied in the aircraft industry for imaging landing flaps in Boeing wings, despite suffering acoustic losses > 120 dB.

The Role of Lung Ultrasound Before and During the COVID-19 Pandemic: A review article

Lung ultrasound [LUS] has evolved considerably over the last years. The aim of the current review is to conduct a systematic review reported from a number of studies to show the usefulness of [LUS] and point of care ultrasound for diagnosing COVID-19. A systematic search of electronic data was conducted including the national library of medicine, and the national institute of medicine, PubMed Central [PMC] to identify the articles depended on [LUS] to monitor COVID-19. This review highlights the ultrasound findings reported in articles before the pandemic [11], clinical articles before COVID-19 [14], review studies during the pandemic [27], clinical cases during the pandemic [5] and other varying aims articles. The reviewed studies revealed that ultrasound findings can be used to help in the detection and staging of the disease. The common patterns observed included irregular and thickened A-lines, multiple B-lines ranging from focal to diffuse interstitial consolidation, and pleural effusion. Sub-plural consolidation is found to be associated with the progression of the disease and its complications. Pneumothorax was not recorded for COVID-19 patients. Further improvement in the diagnostic performance of [LUS] for COVID-19 patients can be achieved by using elastography, contrast-enhanced ultrasound, and power Doppler imaging.

Comparing lung ultrasound: extensive versus short in COVID-19 (CLUES): a multicentre, observational study at the emergency department

BACKGROUND

Bedside lung ultrasound (LUS) is an affordable diagnostic tool that could contribute to identifying COVID-19 pneumonia. Different LUS protocols are currently used at the emergency department (ED) and there is a need to know their diagnostic accuracy.

DESIGN

A multicentre, prospective, observational study, to compare the diagnostic accuracy of three commonly used LUS protocols in identifying COVID-19 pneumonia at the ED.

SETTING/PATIENTS

Adult patients with suspected COVID-19 at the ED, in whom we prospectively performed 12-zone LUS and SARS-CoV-2 reverse transcription PCR.

MEASUREMENTS

We assessed diagnostic accuracy for three different ultrasound protocols using both PCR and final diagnosis as a reference standard.

RESULTS

Between 19 March 2020 and 4 May 2020, 202 patients were included. Sensitivity, specificity and negative predictive value compared with PCR for 12-zone LUS were 91.4% (95% CI 84.4 to 96.0), 83.5% (95% CI 74.6 to 90.3) and 90.0% (95% CI 82.7 to 94.4). For 8-zone and 6-zone protocols, these results were 79.7 (95% CI 69.9 to 87.6), 69.0% (95% CI 59.6 to 77.4) and 81.3% (95% CI 73.8 to 87.0) versus 89.9% (95% CI 81.7 to 95.3), 57.5% (95% CI 47.9 to 66.8) and 87.8% (95% CI 79.2 to 93.2). Negative likelihood ratios for 12, 8 and 6 zones were 0.1, 0.3 and 0.2, respectively. Compared with the final diagnosis specificity increased to 83.5% (95% CI 74.6 to 90.3), 78.4% (95% CI 68.8 to 86.1) and 65.0% (95% CI 54.6 to 74.4), respectively, while the negative likelihood ratios were 0.1, 0.2 and 0.16.

CONCLUSION

Identifying COVID-19 pneumonia at the ED can be aided by bedside LUS. The more efficient 6-zone protocol is an excellent screening tool, while the 12-zone protocol is more specific and gives a general impression on lung involvement.

TRIAL REGISTRATION NUMBER

NL8497.

Can follow up lung ultrasound in Coronavirus Disease-19 patients indicate clinical outcome?

Purpose

To evaluate whether there is a change in findings of coronavirus disease 2019 patients in follow up lung ultrasound and to determine whether these findings can predict the development of severe disease.

Materials and methods

In this prospective monocentric study COVID-19 patients had standardized lung ultrasound (12 area evaluation) at day 1, 3 and 5. The primary end point was detection of pathologies and their change over time. The secondary end point was relationship between change in sonographic results and clinical outcome. Clinical outcome was assessed on development of severe disease defined as need for intensive care unit.

Results

Data of 30 patients were analyzed, 26 patients with follow-up lung ultrasound. All of them showed lung pathologies with dynamic patterns. 26,7% developed severe disease tending to have an ubiquitous lung involvement in lung ultrasound. In patients with need for intensive care unit a previously developed increase in B-lines, subpleural consolidations and pleural line irregularities was more common. A statistically significant association between change in B-lines as well as change in pleural line irregularities and development of severe disease was observed (p<0,01).

Conclusion

The present study demonstrates that follow up lung ultrasound can be a powerful tool to track the evolution of disease and suggests that lung ultrasound is able to indicate an impending development of severe disease in COVID-19 patients.

Prevalence and significance of pulmonary disease on lung ultrasonography in outpatients with SARS-CoV-2 infection

BACKGROUND

The majority of patients with SARS-CoV-2 infection are diagnosed and managed as outpatients; however, little is known about the burden of pulmonary disease in this setting. Lung ultrasound (LUS) is a convenient tool for detection of COVID-19 pneumonia. Identifying SARS-CoV-2 infected outpatients with pulmonary disease may be important for early risk stratification.

OBJECTIVES

To investigate the prevalence, natural history and clinical significance of pulmonary disease in outpatients with SARS-CoV-2.

METHODS

SARS-CoV-2 PCR positive outpatients (CV(+)) were assessed with LUS to identify the presence of interstitial pneumonia. Studies were considered positive based on the presence of B-lines, pleural irregularity and consolidations. A subset of patients underwent longitudinal examinations. Correlations between LUS findings and patient symptoms, demographics, comorbidities and clinical outcomes over 8 weeks were evaluated.

RESULTS

102 CV(+) patients underwent LUS with 42 (41%) demonstrating pulmonary involvement. Baseline LUS severity scores correlated with shortness of breath on multivariate analysis. Of the CV(+) patients followed longitudinally, a majority showed improvement or resolution in LUS findings after 1-2 weeks. Only one patient in the CV(+) cohort was briefly hospitalised, and no patient died or required mechanical ventilation.

CONCLUSION

We found a high prevalence of LUS findings in outpatients with SARS-CoV-2 infection. Given the pervasiveness of pulmonary disease across a broad spectrum of LUS severity scores and lack of adverse outcomes, our findings suggest that LUS may not be a useful as a risk stratification tool in SARS-CoV-2 in the general outpatient population.

A New Lung Ultrasound Protocol Able to Predict Worsening in Patients Affected by Severe Acute Respiratory Syndrome Coronavirus 2 Pneumonia

OBJECTIVES

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can generate severe pneumonia associated with high mortality. A bedside lung ultrasound (LUS) examination has been shown to have a potential role in this setting. The purpose of this study was to evaluate the potential prognostic value of a new LUS protocol (evaluation of 14 anatomic landmarks, with graded scores of 0-3) in patients with SARS-CoV-2 pneumonia and the association of LUS patterns with clinical or laboratory findings.

METHODS

A cohort of 52 consecutive patients with laboratory-confirmed SARS-CoV-2 underwent LUS examinations on admission in an internal medicine ward and before their discharge. A total LUS score as the sum of the scores at each explored area was computed. We investigated the association between the LUS score and clinical worsening, defined as a combination of high-flow oxygen support, intensive care unit admission, or 30-day mortality as the primary end point.

RESULTS

Twenty (39%) patients showed a worse outcome during the observation period; the mean LUS scores ± SDs were 20.4 ± 8.5 and 29.2 ± 7.3 in patients without and with worsening, respectively (P < .001). In a multivariable analysis, adjusted for comorbidities (>2), age (>65 years), sex (male), and body mass index (≥25 kg/m² ), the association between the LUS score and worsening (odds ratio, 1.17; 95% confidence interval, 1.05 to 1.29; P = .003) was confirmed, with good discrimination of the model (area under the receiver operating characteristic curve, 0.82). A median LUS score higher than 24 was associated with an almost 6-fold increase in the odds of worsening (odds ratio, 5.67; 95% confidence interval, 1.29 to 24.8; P = .021).

CONCLUSIONS

Lung ultrasound can represent an effective tool for monitoring and stratifying the prognosis of patients with SARS-CoV-2 pulmonary involvement.

A Comparison of Lung Ultrasound and Computed Tomography in the Diagnosis of Patients with COVID-19: A Systematic Review and Meta-Analysis

Background Lung ultrasound (LUS) and computed tomography (CT) can both be used for diagnosis of interstitial pneumonia caused by coronavirus disease 2019 (COVID-19), but the agreement between LUS and CT is unknown. Purpose to compare the agreement of LUS and CT in the diagnosis of interstitial pneumonia caused by COVID-19. Materials and Methods We searched PubMed, Cochrane library, Embase, Chinese Biomedicine Literature, and WHO COVID-19 databases to identify studies that compared LUS with CT in the diagnosis of interstitial pneumonia caused by COVID-19. We calculated the pooled overall, positive and negative percent agreements, diagnostic odds ratio (DOR) and the area under the standard receiver operating curve (SROC) for LUS in the diagnosis of COVID-19 compared with CT. Results We identified 1896 records, of which nine studies involving 531 patients were finally included. The pooled overall, positive and negative percentage agreements of LUS for the diagnosis of interstitial pneumonia caused by COVID-19 compared with CT were 81% (95% confidence interval [CI] 43–99%), 96% (95% CI, 80–99%, I² = 92.15%) and 80% (95%CI, 60–92%, I² = 92.85%), respectively. DOR was 37.41 (95% CI, 9.43–148.49, I² = 63.9%), and the area under the SROC curve was 0.94 (95% CI, 0.92–0.96). The quality of evidence for both specificity and sensitivity was low because of heterogeneity and risk of bias. Conclusion The level of diagnostic agreement between LUS and CT in the diagnosis of interstitial pneumonia caused by COVID-19 is high. LUS can be therefore considered as an equally accurate alternative for CT in situations where molecular tests are not available.

A single-center comparative study of lung ultrasound versus chest computed tomography during the COVID-19 era

Background

Lung ultrasound (LUS) is a bedside imaging tool that has proven useful in identifying and assessing the severity of pulmonary pathology. The aim of this study was to determine LUS patterns, their clinical significance, and how they compare to CT findings in hospitalized patients with coronavirus infection.

Methods

This observational study included 62 patients (33 men, age 59.3±15.9 years), hospitalized with pneumonia due to COVID-19, who underwent chest CT and bedside LUS on the day of admission. The CT images were analyzed by chest radiographers who calculated a CT visual score based on the expansion and distribution of ground-glass opacities and consolidations. The LUS score was calculated according to the presence, distribution, and severity of anomalies.

Results

All patients had CT findings suggestive of bilateral COVID-19 pneumonia, with an average visual scoring of 8.1±2.9%. LUS identified 4 different abnormalities, with bilateral distribution (mean LUS score: 26.4±6.7), focal areas of non-confluent B lines, diffuse confluent B lines, small sub-pleural micro consolidations with pleural line irregularities, and large parenchymal consolidations with air bronchograms. LUS score was significantly correlated with CT visual scoring (rho = 0.70; p<0.001). Correlation analysis of the CT and LUS severity scores showed good interclass correlation (ICC) (ICC =0.71; 95% confidence interval (CI): 0.52–0.83; p<0.001). Logistic regression was used to determine the cut-off value of ≥27 (area under the curve: 0.97; 95% CI: 90-99; sensitivity 88.5% and specificity 97%) of the LUS severity score that represented severe and critical pulmonary involvement on chest CT (CT: 3-4).

Conclusion

When combined with clinical data, LUS can provide a potent diagnostic aid in patients with suspected COVID-19 pneumonia, reflecting CT findings.

Comparison of two different lung ultrasound imaging protocols in COVID-19 pneumonia

Purpose

The aim of this study was to determine the effectiveness of two different lung ultrasonography (LUS) methods that can be used in the diagnosis of coronavirus disease 2019 (COVID-19) and to investigate their correlations with computed tomography (CT).

Methods

In this prospective, randomized, and single-blind study, 60 patients with COVID-19 were included. The patients were randomized to either the 12-zone LUS group (n=30) or the 14-zone LUS group (n=30). The correlation between LUS and thoracic CT scores was evaluated. As a secondary outcome measure, the characteristic features of the findings of thoracic CT and LUS were examined.

Results

The study was completed with a total of 59 patients. Moderate and high correlations were found between the total CT and LUS scores in the 12-zone and 14-zone study groups. There were no statistically significant differences in the lesion types detected in patients using LUS and CT (P>0.05). The left lung lower lobe CT scores were statistically significantly lower in the 14-zone study group than in the 12-zone group (P=0.019). The left lower lobe CT and LUS scores were highly correlated in the 14-zone group (P<0.001, r=0.902).

Conclusion

The results of our study indicated that the two different LUS examination methods performed in different patients had similar findings in terms of the diagnosis and their correlations with CT results.

Implementation of lung ultrasound in the triage of pregnant women during the SARS-CoV-2 pandemics

Lung ultrasound (US) has been shown that it is able to detect interstitial lung disease, subpleural consolidations and acute respiratory distress syndrome in clinical and physical studies that assess its role in upper respiratory infections. It is used worldwide in the coronavirus disease 2019 (COVID-19) outbreak and the effectiveness has been assessed in several studies. Fast diagnosis of COVID-19 is essential in deciding for patient isolation, clinical care and reducing transmission. Imaging the lung and pleura by ultrasound is efficient, cost-effective, and safe and it is recognized as rapid, repeatable, and reliable. Obstetricians are already using the US and are quite proficient in doing so. During the pandemic, performing lung US (LUS) right after the fetal assessment until reverse transcription polymerase chain reaction results are obtained, particularly in settings that have a centralized testing center, was found feasible for the prediction of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The use of LUS is efficient in the triage and monitoring of pregnant women. Clinicians dealing with pregnant women should consider LUS as the first-line diagnostic tool in pregnant women during the SARS-CoV-2 pandemic.

Lung Ultrasound in COVID-19: Clinical Correlates and Comparison with Chest Computed Tomography

Lung ultrasound (LUS) and chest computed tomography (chest CT) are largely employed to evaluate coronavirus disease 2019 (COVID-19) pneumonia. We investigated semi-quantitative LUS and CT scoring in hospitalized COVID-19 patients. LUS and chest CT were performed within 24 h upon admission. Both were analyzed according to semi-quantitative scoring systems. Subgroups were identified according to median LUS score. Patients within higher LUS score group were older (79 vs 60 years, p<0.001), had higher C-reactive protein (CRP) (7.2 mg/dl vs 1.3 mg/dl, p<0.001) and chest CT score (10 vs 4, p=0.027) as well as lower PaO2/FiO2 (286 vs 356, p=0.029) as compared to patients within lower scores. We found a significant correlation between scores (r=0.390, p=0.023). Both LUS and CT scores correlated directly with patients age (r=0.586, p<0.001 and r=0.399, p=0.021 respectively) and CRP (r=0.472, p=0.002 and r=0.518, p=0.002 respectively), inversely with PaO2/FiO2 (r=-0.485, p=0.003 and r=-0.440, p=0.017 respectively). LUS score only showed significant correlation with hs-troponin T, NT-pro-BNP, and creatinine (r=0.433, p=0.019; r=0.411, p=0.027, and r=0.497, p=0.001, respectively). Semi-quantitative bedside LUS is related to the severity of COVID-19 pneumonia similarly to chest CT. Correlation of LUS score with markers of cardiac and renal injury suggests that LUS might contribute to a more comprehensive evaluation of this heterogeneous population.

Coronavirus Disease 2019 in Children

Since its appearance in Wuhan in mid-December 2019, acute respiratory syndrome coronavirus 2 (SARS-CoV-2) related 19 coronavirus disease (COVID-19) has spread dramatically worldwide. It soon became apparent that the incidence of pediatric COVID-19 was much lower than the adult form. Morbidity in children is characterized by a variable clinical presentation and course. Symptoms are similar to those of other acute respiratory viral infections, the upper airways being more affected than the lower airways. Thus far, over 90% of children who tested positive for the virus presented mild or moderate symptoms and signs. Most children were asymptomatic, and only a few cases were severe, unlike in the adult population. Deaths have been rare and occurred mainly in children with underlying morbidity. Factors as reduced angiotensin-converting enzyme receptor expression, increased activation of the interferon-related innate immune response, and trained immunity have been implicated in the relative resistance to COVID-19 in children, however the underlying pathogenesis and mechanism of action remain to be established. While at the pandemic outbreak, mild respiratory manifestations were the most frequently described symptoms in children, subsequent reports suggested that the clinical course of COVID-19 is more complex than initially thought. Thanks to the experience acquired in adults, the diagnosis of pediatric SARS-CoV-2 infection has improved with time. Data on the treatment of children are sparse, however, several antiviral trials are ongoing. The purpose of this narrative review is to summarize current understanding of pediatric SARS-CoV-2 infection and provide more accurate information for healthcare workers and improve the care of patients.

Highlighting COVID-19: What the imaging exams show about the disease

Coronavirus disease 2019 (COVID-19), a global emergency, is caused by severe acute respiratory syndrome coronavirus 2. The gold standard for its diagnosis is the reverse transcription polymerase chain reaction, but considering the high number of infected people, the low availability of this diagnostic tool in some contexts, and the limitations of the test, other tools that aid in the identification of the disease are necessary. In this scenario, imaging exams such as chest X-ray (CXR) and computed tomography (CT) have played important roles. CXR is useful for assessing disease progression because it allows the detection of extensive consolidations, besides being a fast and cheap method. On the other hand, CT is more sensitive for detecting lung changes in the early stages of the disease and is also useful for assessing disease progression. Of note, ground-glass opacities are the main COVID-19-related CT findings. Positron emission tomography combined with CT can be used to evaluate chronic and substantial damage to the lungs and other organs; however, it is an expensive test. Lung ultrasound (LUS) has been shown to be a promising technique in that context as well, being useful in the screening and monitoring of patients, disease classification, and management related to mechanical ventilation. Moreover, LUS is an inexpensive alternative available at the bedside. Finally, magnetic resonance imaging, although not usually requested, allows the detection of pulmonary, cardiovascular, and neurological abnormalities associated with COVID-19. Furthermore, it is important to consider the challenges faced in the radiology field in the adoption of control measures to prevent infection and in the follow-up of post-COVID-19 patients.

Interstitial lung opacities in patients with severe COVID-19 pneumonia by bedside high-resolution ultrasound in association to CO2 retention

BACKGROUND

Coronavirus disease 2019 (COVID-19) can cause acute respiratory distress syndrome (ARDS).

OBJECTIVE

This single centre cross-section study aimed to grade the severity of pneumonia by bed-side lung ultrasound (LUS).

METHODS

A scoring system discriminates 5 levels of lung opacities: A-lines (0 points),≥3 B-line (1 point), coalescent B-lines (2 points), marked pleural disruptions (3 points), consolidations (4 points). LUS (convex 1-5 MHz probe) was performed at 6 defined regions for each hemithorax either in supine or prone position. A lung aeration score (LAS, maximum 4 points) was allocated for each patient by calculating the arithmetic mean of the examined lung areas. Score levels were correlated with ventilation parameters and laboratory markers.

RESULTS

LAS of 20 patients with ARDS reached from 2.58 to 3.83 and was highest in the lateral right lobe (Mean 3.67). Ferritin levels (Mean 1885μg/l; r = 0.467; p = 0.051) showed moderate correlation in spearman roh calculation. PaCO2 level (Mean 46.75 mmHg; r = 0.632; p = 0.005) correlated significantly with LAS, while duration of ventilation, Horovitz index, CRP, LDH and IL-6 did not.

CONCUSIONS

The proposed LAS describes severity of lung opacities in COVID-19 patients and correlates with CO2 retention in patients with ARDS.

Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic

The advancement of technology remained an immersive interest for humankind throughout the past decades. Tech enterprises offered a stream of innovation to address the universal healthcare concerns. The novel coronavirus holds a substantial foothold of planet earth which is combatted by digital interventions across afflicted geographical boundaries and territories. This study aims to explore the trends of modern healthcare technologies and Artificial Intelligence (AI) during COVID-19 crisis, define the concepts and clinical role of AI in the mitigation of COVID-19, investigate and correlate the efficacy of AI-enabled technology in medical imaging during COVID-19 and determine advantages, drawbacks, and challenges of artificial intelligence during COVID-19 pandemic. The paper applied systematic review approach using a deliberated research protocol and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart. Digital technologies can coordinate COVID-19 responses in a cascade fashion that extends from the clinical care facility to the exterior of the pending viral epicenter. With cases of healthcare robotics, aerial drones, and the internet of things as evidentiary examples. PCR tests and medical imaging are the frontier diagnostics of COVID-19. Computed tomography helped to correct the accuracy variation of PCR tests at a clinical sensitivity of 98 %. Artificial intelligence can enable autonomous COVID-19 responses using techniques like machine learning. Technology could be an endless system of innovation and opportunities when sourced effectively. Scientists can utilize technology to resolve global concerns challenging the history of tangible possibility. Digital interventions have enhanced the responses to COVID-19, magnified the role of medical imaging amid the COVID-19 crisis and have exposed healthcare professionals to the opportunity of contactless care.

One-month outcomes of patients with SARS-CoV-2 infection and their relationships with lung ultrasound signs

BACKGROUND

The role of lung ultrasound (LUS) in evaluating the mid- and long-term prognoses of patients with COVID-19 pneumonia is not yet known. The objectives of this study were to evaluate associations between LUS signs at the time of screening and clinical outcomes 1 month after LUS and to assess LUS signs at the time of presentation with known risk factors for COVID-19 pneumonia.

METHODS

This was a retrospective study of data prospectively collected 1 month after LUS screening of 447 adult patients diagnosed with COVID-19 pneumonia. Sonographic examination was performed in screening tents with the participants seated. The LUS signs (B-lines > 2, coalescent B-lines, and subpleural consolidations) were captured in six areas of each hemithorax and a LUS aeration score was calculated; in addition, the categories of disease probability based on patterns of LUS findings (high-probability, intermediate-probability, alternate, and low-probability patterns) were evaluated. The LUS signs at patients' initial evaluation were related to the following outcomes: symptomatology, the need for hospitalization or invasive mechanical ventilation (IMV), and COVID-19-related death.

RESULTS

According to the evaluations performed 1 month after LUS screening, 36 patients were hospitalised, eight of whom required intensive care unit (ICU) admission and three of whom died. The presence of coalescent B-lines was associated with the need for hospitalization (p = 0.008). The presence of subpleural consolidations was associated with dyspnoea (p < 0.0001), cough (p = 0.003), the need for hospitalization (p < 0.0001), the need for ICU admission (p < 0.0001), and death (p = 0.002). A higher aeration score was associated with dyspnoea (p < 0.0001), the need for hospitalization (p < 0.0001), the need for ICU admission (p < 0.0001), and death (p = 0.003). In addition, patients with a high-probability LUS pattern had a higher aeration score (p < 0.0001) and more dyspnoea (p = 0.024) and more often required hospitalization (p < 0.0001) and ICU admission (p = 0.031).

CONCLUSIONS

In patients with COVID-19 pneumonia, LUS signs were related to respiratory symptoms 1 month after LUS screening. Strong relationships were identified between LUS signs and the need for hospitalization and death.

Quantitative pleural line characterization outperforms traditional lung texture ultrasound features in detection of COVID-19

BACKGROUND AND OBJECTIVE

Lung ultrasound is an inherently user-dependent modality that could benefit from quantitative image analysis. In this pilot study we evaluate the use of computer-based pleural line (p-line) ultrasound features in comparison to traditional lung texture (TLT) features to test the hypothesis that p-line thickening and irregularity are highly suggestive of coronavirus disease 2019 (COVID-19) and can be used to improve the disease diagnosis on lung ultrasound.

METHODS

Twenty lung ultrasound images, including normal and COVID-19 cases, were used for quantitative analysis. P-lines were detected by a semiautomated segmentation method. Seven quantitative features describing thickness, margin morphology, and echo intensity were extracted. TLT lines were outlined, and texture features based on run-length and gray-level co-occurrence matrix were extracted. The diagnostic performance of the 2 feature sets was measured and compared using receiver operating characteristics curve analysis. Observer agreements were evaluated by measuring interclass correlation coefficients (ICC) for each feature.

RESULTS

Six of 7 p-line features showed a significant difference between normal and COVID-19 cases. Thickness of p-lines was larger in COVID-19 cases (6.27 ± 1.45 mm) compared to normal (1.00 ± 0.19 mm), P < 0.001. Among features describing p-line margin morphology, projected intensity deviation showed the largest difference between COVID-19 cases (4.08 ± 0.32) and normal (0.43 ± 0.06), P < 0.001. From the TLT line features, only 2 features, gray-level non-uniformity and run-length non-uniformity, showed a significant difference between normal cases (0.32 ± 0.06, 0.59 ± 0.06) and COVID-19 (0.22 ± 0.02, 0.39 ± 0.05), P = 0.04, respectively. All features together for p-line showed perfect sensitivity and specificity of 100; whereas, TLT features had a sensitivity of 90 and specificity of 70. Observer agreement for p-lines (ICC = 0.65-0.85) was higher than for TLT features (ICC = 0.42-0.72).

CONCLUSION

P-line features characterize COVID-19 changes with high accuracy and outperform TLT features. Quantitative p-line features are promising diagnostic tools in the interpretation of lung ultrasound images in the context of COVID-19.

Modality alignment contrastive learning for severity assessment of COVID-19 from lung ultrasound and clinical information

The outbreak of COVID-19 around the world has caused great pressure to the health care system, and many efforts have been devoted to artificial intelligence (AI)-based analysis of CT and chest X-ray images to help alleviate the shortage of radiologists and improve the diagnosis efficiency. However, only a few works focus on AI-based lung ultrasound (LUS) analysis in spite of its significant role in COVID-19. In this work, we aim to propose a novel method for severity assessment of COVID-19 patients from LUS and clinical information. Great challenges exist regarding the heterogeneous data, multi-modality information, and highly nonlinear mapping. To overcome these challenges, we first propose a dual-level supervised multiple instance learning module (DSA-MIL) to effectively combine the zone-level representations into patient-level representations. Then a novel modality alignment contrastive learning module (MA-CLR) is presented to combine representations of the two modalities, LUS and clinical information, by matching the two spaces while keeping the discriminative features. To train the nonlinear mapping, a staged representation transfer (SRT) strategy is introduced to maximumly leverage the semantic and discriminative information from the training data. We trained the model with LUS data of 233 patients, and validated it with 80 patients. Our method can effectively combine the two modalities and achieve accuracy of 75.0% for 4-level patient severity assessment, and 87.5% for the binary severe/non-severe identification. Besides, our method also provides interpretation of the severity assessment by grading each of the lung zone (with accuracy of 85.28%) and identifying the pathological patterns of each lung zone. Our method has a great potential in real clinical practice for COVID-19 patients, especially for pregnant women and children, in aspects of progress monitoring, prognosis stratification, and patient management.

Lung Ultrasound: The Essentials

Although US of the lungs is increasingly used clinically, diagnostic radiologists are not routinely trained in its use and interpretation. Lung US is a highly sensitive and specific modality that aids in the evaluation of the lungs for many different abnormalities, including pneumonia, pleural effusion, pulmonary edema, and pneumothorax. This review provides an overview of lung US to equip the diagnostic radiologist with knowledge needed to interpret this increasingly used modality. Supplemental material is available for this article. © RSNA, 2021.

A Programmable Platform for Accelerating the Development of Smart Ultrasound Transducer Probe

During the COVID-19 pandemic, an ultraportable ultrasound smart probe has proven to be one of the few practical diagnostic and monitoring tools for doctors who are fully covered with personal protective equipment. The real-time, safety, ease of sanitization, and ultraportability features of an ultrasound smart probe make it extremely suitable for diagnosing COVID-19. In this article, we discuss the implementation of a smart probe designed according to the classic architecture of ultrasound scanners. The design balanced both performance and power consumption. This programmable platform for an ultrasound smart probe supports a 64-channel full digital beamformer. The platform's size is smaller than 10 cm ×5 cm. It achieves a 60-dBFS signal-to-noise ratio (SNR) and an average power consumption of ~4 W with 80% power efficiency. The platform is capable of achieving triplex B-mode, M-mode, color, pulsed-wave Doppler mode imaging in real time. The hardware design files are available for researchers and engineers for further study, improvement or rapid commercialization of ultrasound smart probes to fight COVID-19.

Point of Care Ultrasound in Coronavirus Disease 2019 Pandemic: One Modality Helping Multiple Specialties

After the detection of novel coronavirus (2019) as the cause of a cluster of pneumonia in Wuhan, China, at the end of 2019, more than 10 million confirmed cases of coronavirus disease 2019 (COVID-19) have been reported around the globe. In the COVID-19 intensive care unit (ICU), the use of stethoscope is minimal for obvious reasons. Shifting of COVID-19 patients out of ICU setup increases the risk of transmission of infection to health-care workers as well as jeopardizes the safety of patients. Hence, diagnostic imaging has emerged as a fundamental component of the current management of COVID-19. Lung ultrasound (LUS) imaging has become a safe bedside imaging alternative that does not expose the patient to radiation and minimizes the risk of contamination. Ultrasound (USG) can be used to scan almost all vital organs (heart, kidney, vascular, brain, etc.) and also help in rapid decision-making regarding the management of COVID-19 patients. In this note, we review the current state of the art of LUS in evaluating pulmonary changes induced by COVID-19. The goal is to identify characteristic sonographic findings most suited for the diagnosis of COVID-19 pneumonia infections as well as to assess the impact of infection on other organs and utilizing the same in the management of COVID patients without compromising on the safety of patient or health-care provider.

Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis

Care during the COVID-19 pandemic hinges upon the existence of fast, safe, and highly sensitive diagnostic tools. Considering significant practical advantages of lung ultrasound (LUS) over other imaging techniques, but difficulties for doctors in pattern recognition, we aim to leverage machine learning toward guiding diagnosis from LUS. We release the largest publicly available LUS dataset for COVID-19 consisting of 202 videos from four classes (COVID-19, bacterial pneumonia, non-COVID-19 viral pneumonia and healthy controls). On this dataset, we perform an in-depth study of the value of deep learning methods for the differential diagnosis of lung pathologies. We propose a frame-based model that correctly distinguishes COVID-19 LUS videos from healthy and bacterial pneumonia data with a sensitivity of 0.90±0.08 and a specificity of 0.96±0.04. To investigate the utility of the proposed method, we employ interpretability methods for the spatio-temporal localization of pulmonary biomarkers, which are deemed useful for human-in-the-loop scenarios in a blinded study with medical experts. Aiming for robustness, we perform uncertainty estimation and demonstrate the model to recognize low-confidence situations which also improves performance. Lastly, we validated our model on an independent test dataset and report promising performance (sensitivity 0.806, specificity 0.962). The provided dataset facilitates the validation of related methodology in the community and the proposed framework might aid the development of a fast, accessible screening method for pulmonary diseases. Dataset and all code are publicly available at: https://github.com/BorgwardtLab/covid19_ultrasound.

Point-of-care ultrasound in the COVID-19 era: A scoping review

In the midst of the COVID-19 pandemic, unprecedented pressure has been added to healthcare systems around the globe. Imaging is a crucial component in the management of COVID-19 patients. Point-of-care ultrasound (POCUS) such as hand-carried ultrasound emerges in the COVID-19 era as a tool that can simplify the imaging process of COVID-19 patients, and potentially reduce the strain on healthcare providers and healthcare resources. The preliminary evidence available suggests an increasing role of POCUS in diagnosing, monitoring, and risk-stratifying COVID-19 patients. This scoping review aims to delineate the challenges in imaging COVID-19 patients, discuss the cardiopulmonary complications of COVID-19 and their respective sonographic findings, and summarize the current data and recommendations available. There is currently a critical gap in knowledge in the role of POCUS in the COVID-19 era. Nonetheless, it is crucial to summarize the current preliminary data available in order to help fill this gap in knowledge for future studies.

Comparison of admission chest computed tomography and lung ultrasound performance for diagnosis of COVID-19 pneumonia in populations with different disease prevalence

PURPOSE

Chest computed tomography (CT) is considered a reliable imaging tool for COVID-19 pneumonia diagnosis, while lung ultrasound (LUS) has emerged as a potential alternative to characterize lung involvement. The aim of the study was to compare diagnostic performance of admission chest CT and LUS for the diagnosis of COVID-19.

METHODS

We included patients admitted to emergency department between February 21-March 6, 2020 (high prevalence group, HP) and between March 30-April 13, 2020 (moderate prevalence group, MP) undergoing LUS and chest CT within 12 h. Chest CT was considered positive in case of "indeterminate"/"typical" pattern for COVID-19 by RSNA classification system. At LUS, thickened pleural line with ≥ three B-lines at least in one zone of the 12 explored was considered positive. Sensitivity, specificity, PPV, NPV, and AUC were calculated for CT and LUS against real-time reverse transcriptase polymerase chain reaction (RT-PCR) and serology as reference standard.

RESULTS

The study included 486 patients (males 61 %; median age, 70 years): 247 patients in HP (COVID-19 prevalence 94 %) and 239 patients in MP (COVID-19 prevalence 45 %). In HP and MP respectively, sensitivity, specificity, PPV, and NPV were 90-95 %, 43-69 %, 96-72 %, 20-95 % for CT and 94-93 %, 7-31 %, 94-52 %, 7-83 % for LUS. CT demonstrated better performance than LUS in diagnosis of COVID-19, both in HP (AUC 0.75 vs 0.51; P < 0.001) and MP (AUC 0.85 vs 0.62; P < 0.001).

CONCLUSIONS

Admission chest CT shows better performance than LUS for COVID-19 diagnosis, at varying disease prevalence. LUS is highly sensitive, but not specific for COVID-19.

Current Ultrasound Technologies and Instrumentation in the Assessment and Monitoring of COVID-19 Positive Patients

Since the emergence of the COVID-19 pandemic in December of 2019, clinicians and scientists all over the world have faced overwhelming new challenges that not only threaten their own communities and countries but also the world at large. These challenges have been enormous and debilitating, as the infrastructure of many countries, including developing ones, had little or no resources to deal with the crisis. Even in developed countries, such as Italy, health systems have been so inundated by cases that health care facilities became oversaturated and could not accommodate the unexpected influx of patients to be tested. Initially, resources were focused on testing to identify those who were infected. When it became clear that the virus mainly attacks the lungs by causing parenchymal changes in the form of multifocal pneumonia of different levels of severity, imaging became paramount in the assessment of disease severity, progression, and even response to treatment. As a result, there was a need to establish protocols for imaging of the lungs in these patients. In North America, the focus was on chest X-ray and computed tomography (CT) as these are widely available and accessible at most health facilities. However, in Europe and China, this was not the case, and a cost-effective and relatively fast imaging modality was needed to scan a large number of sick patients promptly. Hence, ultrasound (US) found its way into the hands of Chinese and European physicians and has since become an important imaging modality in those locations. US is a highly versatile, portable, and inexpensive imaging modality that has application across a broad spectrum of conditions and, in this way, is ideally suited to assess the lungs of COVID-19 patients in the intensive care unit (ICU). This bedside test can be done with little to no movement of the patients from the unit that keeps them in their isolated rooms, thereby limiting further exposure to other health personnel. This article presents a basic introduction to COVID-19 and the use of the US for lung imaging. It further provides a high-level overview of the existing US technologies that are driving development in current and potential future US imaging systems for lung, with a specific emphasis on portable and 3-D systems.

Imaging of COVID-19 pneumonia in children

OBJECTIVE

Literature related to the imaging of COVID-19 pneumonia, its findings and contribution to diagnosis and its differences from adults are limited in pediatric patients. The aim of this study was to evaluate chest X-ray and chest CT findings in children with COVID-19 pneumonia.

METHODS

Chest X-ray findings of 59 pediatric patients and chest CT findings of 22 patients with a confirmed diagnosis of COVID-19 pneumonia were evaluated retrospectively.

RESULTS

COVID-19 pneumonia was most commonly observed unilaterally and in lower zones of lungs in chest X-ray examinations. Bilateral and multifocal involvement (55%) was the most observed involvement in the CT examinations, as well as, single lesion and single lobe (27%) involvement were also detected. Pure ground-glass appearance was observed in 41%, ground-glass appearance and consolidation together was in 36%. While peripheral and central co-distribution of the lesions (55%) were frequently observed, the involvement of the lower lobes (69%) was significant. In four cases,the coexistence of multiple rounded multifocal ground-glass appearance and rounded consolidation were observed.

CONCLUSION

COVID-19 pneumonia imaging findings may differ in the pediatric population from adults. In diagnosis, chest X-ray should be preferred, CT should be requested if there is a pathologic finding on radiography that merits further evaluation and if clinically indicated.

ADVANCES IN KNOWLEDGE

Radiological findings of COVID-19 observed in children may differ from adults. Chest X-ray should often be sufficient in children avoiding additional irradiation, chest CT needs only be done in cases of clinical necessity.

Lung ultrasonography in pregnant women during the COVID-19 pandemic: an interobserver agreement study among obstetricians

Purpose

This study investigated interobserver agreement in lung ultrasonography (LUS) in pregnant women performed by obstetricians with different levels of expertise, with confirmation by an expert radiologist.

Methods

This prospective study was conducted at a tertiary "Coronavirus Pandemic Hospital" in April 2020. Pregnant women suspected to have coronavirus disease 2019 (COVID-19) were included. Two blinded experienced obstetricians performed LUS on pregnant women separately and noted their scores for 14 lung zones. Following a theoretical and hands-on practical course, one experienced obstetrician, two novice obstetric residents, and an experienced radiologist blindly evaluated anonymized and randomized still images and videoclips retrospectively. Weighted Cohen's kappa and Krippendorff’s alpha tests were used to assess the interobserver agreement.

Results

Fifty-two pregnant women were included, with confirmed COVID-19 diagnosis rate of 82.7%. In total, 336 eligible still images and 115 videoclips were included in the final analysis. The overall weighted Cohen’s kappa values ranged from 0.706 to 0.912 for the 14 lung zones. There were only seven instances of major disagreement (>1 point) in the evaluation of 14 lung zones of 52 patients (n=728). The overall agreement between the radiologist and obstetricians for the still images (Krippendorff's α=0.856, 95% confidence interval [CI], 0.797 to 0.915) and videoclips (Krippendorff's α=0.785; 95% CI, 0.709 to 0.861) was good.

Conclusion

The interobserver agreement between obstetricians with different levels of experience on still images and videoclips of LUS was good. Following a brief theoretical course, obstetricians' performance of LUS in pregnant women and interpretation of pre-acquired LUS images can be considered consistent.