Diagnostic use of lung ultrasound compared to chest radiograph for suspected pneumonia in a resource-limited setting

Evaluation of the diagnostic performance of physician lung ultrasound versus chest radiography for pneumonia diagnosis in a peri-urban South African cohort

BACKGROUND

Lung ultrasound (US), which is radiation-free and cheaper than chest radiography (CXR), may be a useful modality for the diagnosis of pediatric pneumonia, but there are limited data from low- and middle-income countries.

OBJECTIVES

The aim of this study was to evaluate the diagnostic performance of non-radiologist, physician-performed lung US compared to CXR for pneumonia in children in a resource-constrained, African setting.

MATERIALS AND METHODS

Children under 5 years of age enrolled in a South African birth cohort study, the Drakenstein Child Health Study, who presented with clinically defined pneumonia and had a CXR performed also had a  lung US performed by a study doctor. Each modality was reported by two readers, using standardized methodology. Agreement between modalities, accuracy (sensitivity and specificity) of lung US and inter-rater agreement were assessed. Either consolidation or any abnormality (consolidation or interstitial picture) was considered as endpoints. In the 98 included cases (median age: 7.2 months; 53% male; 69% hospitalized), prevalence was 37% vs. 39% for consolidation and 52% vs. 76% for any abnormality on lung US and CXR, respectively. Agreement between modalities was poor for consolidation (observed agreement=61%, Kappa=0.18, 95% confidence interval [95% CI]: - 0.02 to 0.37) and for any abnormality (observed agreement=56%, Kappa=0.10, 95% CI: - 0.07 to 0.28). Using CXR as the reference standard, sensitivity of lung US was low for consolidation (47%, 95% CI: 31-64%) or any abnormality (5%, 95% CI: 43-67%), while specificity was moderate for consolidation (70%, 95% CI: 57-81%), but lower for any abnormality (58%, 95% CI: 37-78%). Overall inter-observer agreement of CXR was poor (Kappa=0.25, 95% CI: 0.11-0.37) and was significantly lower than the substantial agreement of lung US (Kappa=0.61, 95% CI: 0.50-0.75). Lung US demonstrated better agreement than CXR for all categories of findings, showing a significant difference for consolidation (Kappa=0.72, 95% CI: 0.58-0.86 vs. 0.32, 95% CI: 0.13-0.51).

CONCLUSION

Lung US identified consolidation with similar frequency to CXR, but there was poor agreement between modalities. The significantly higher inter-observer agreement of LUS compared to CXR supports the utilization of lung US by clinicians in a low-resource setting.

Parenchymal Cavitations in Pulmonary Tuberculosis: Comparison between Lung Ultrasound, Chest X-ray and Computed Tomography

This article aims to detect lung cavitations using lung ultrasound (LUS) in a cohort of patients with pulmonary tuberculosis (TB) and correlate the findings with chest computed tomography (CT) and chest X-ray (CXR) to obtain LUS diagnostic sensitivity. Patients with suspected TB were enrolled after being evaluated with CXR and chest CT. A blinded radiologist performed LUS within 3 days after admission at the Infectious Diseases Department. Finally, 82 patients were enrolled in this study. Bronchoalveolar lavage (BAL) confirmed TB in 58/82 (71%). Chest CT showed pulmonary cavitations in 38/82 (43.6%; 32 TB patients and 6 non-TB ones), LUS in 15/82 (18.3%; 11 TB patients and 4 non-TB ones) and CXR in 27/82 (33%; 23 TB patients and 4 non-TB ones). Twelve patients with multiple cavitations were detected with CT and only one with LUS. LUS sensitivity was 39.5%, specificity 100%, PPV 100% and NPV 65.7%. CXR sensitivity was 68.4% and specificity 97.8%. No false positive cases were found. LUS sensitivity was rather low, as many cavitated consolidations did not reach the pleural surface. Aerated cavitations could be detected with LUS with relative confidence, highlighting a thin air crescent sign towards the pleural surface within a hypoechoic area of consolidation, easily distinguishable from a dynamic or static air bronchogram.

Point-of-care lung ultrasound in detecting pneumonia: A systematic review

Purpose

Limited evidence exists to assess the sensitivity, specificity, and accuracy of point-of-care lung ultrasound (LUS) across all age groups. This review aimed to investigate the benefits of point-of-care LUS for the early diagnosis of pneumonia compared to traditional chest X-rays (CXR) in a subgroup analysis including pediatric, adult, and geriatric populations.

Material and Methods

This systematic review examined systematic reviews, meta-analyses, and original research from 2017 to 2021, comparing point-of-care LUS and CXR in diagnosing pneumonia among adults, pediatrics and geriatrics. Studies lacking direct comparison or exploring diseases other than pneumonia, case reports, and those examining pneumonia secondary to COVID-19 variants were excluded. The search utilized PubMed, Google Scholar, and Cochrane databases with specific search strings. The study selection, conducted by two independent investigators, demonstrated an agreement by the Kappa index, ensuring reliable article selection. The QUADAS-2 tool assessed the selected studies for quality, highlighting risk of bias and applicability concerns across key domains. Statistical analysis using Stata Version 16 determined pooled sensitivity and specificity via a bivariate model, emphasizing LUS and CXR diagnostic capabilities. Additionally, RevMan 5.4.1 facilitated the calculation of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), offering insights into diagnostic accuracy.

Results

The search, conducted across PubMed, Google Scholar, and Cochrane Library databases by two independent investigators, initially identified 1045 articles. Following screening processes, 12 studies comprised a sample size of 2897. LUS demonstrated a likelihood ratio of 5.09, a specificity of 81.91%, and a sensitivity of 92.13% in detecting pneumonia in pediatric, adult, and geriatric patients, with a p-value of 0.0002 and a 95% confidence interval, indicating diagnostic accuracy ranging from 84.07% to 96.29% when compared directly to CXR.

Conclusion

Our review supports that LUS can play a valuable role in detecting pneumonia early with high sensitivity, specificity, and diagnostic accuracy across diverse patient demographics, including pediatric, adult, and geriatric populations. Since it overcomes most of the limitations of CXR and other diagnostic modalities, it can be utilized as a diagnostic tool for pneumonia for all age groups as it is a safe, readily available, and cost-effective modality that can be utilized in an emergency department, intensive care units, wards, and clinics by trained respiratory care professionals.

PLUS-IS-LESS project: Procalcitonin and Lung UltraSonography-based antibiotherapy in patients with Lower rESpiratory tract infection in Swiss Emergency Departments: study protocol for a pragmatic stepped-wedge cluster-randomized trial

BACKGROUND

Lower respiratory tract infections (LRTIs) are among the most frequent infections and a significant contributor to inappropriate antibiotic prescription. Currently, no single diagnostic tool can reliably identify bacterial pneumonia. We thus evaluate a multimodal approach based on a clinical score, lung ultrasound (LUS), and the inflammatory biomarker, procalcitonin (PCT) to guide prescription of antibiotics. LUS outperforms chest X-ray in the identification of pneumonia, while PCT is known to be elevated in bacterial and/or severe infections. We propose a trial to test their synergistic potential in reducing antibiotic prescription while preserving patient safety in emergency departments (ED).

METHODS

The PLUS-IS-LESS study is a pragmatic, stepped-wedge cluster-randomized, clinical trial conducted in 10 Swiss EDs. It assesses the PLUS algorithm, which combines a clinical prediction score, LUS, PCT, and a clinical severity score to guide antibiotics among adults with LRTIs, compared with usual care. The co-primary endpoints are the proportion of patients prescribed antibiotics and the proportion of patients with clinical failure by day 28. Secondary endpoints include measurement of change in quality of life, length of hospital stay, antibiotic-related side effects, barriers and facilitators to the implementation of the algorithm, cost-effectiveness of the intervention, and identification of patterns of pneumonia in LUS using machine learning.

DISCUSSION

The PLUS algorithm aims to optimize prescription of antibiotics through improved diagnostic performance and maximization of physician adherence, while ensuring safety. It is based on previously validated tests and does therefore not expose participants to unforeseeable risks. Cluster randomization prevents cross-contamination between study groups, as physicians are not exposed to the intervention during or before the control period. The stepped-wedge implementation of the intervention allows effect calculation from both between- and within-cluster comparisons, which enhances statistical power and allows smaller sample size than a parallel cluster design. Moreover, it enables the training of all centers for the intervention, simplifying implementation if the results prove successful. The PLUS algorithm has the potential to improve the identification of LRTIs that would benefit from antibiotics. When scaled, the expected reduction in the proportion of antibiotics prescribed has the potential to not only decrease side effects and costs but also mitigate antibiotic resistance.

TRIAL REGISTRATION

This study was registered on July 19, 2022, on the ClinicalTrials.gov registry using reference number: NCT05463406.

TRIAL STATUS

Recruitment started on December 5, 2022, and will be completed on November 3, 2024. Current protocol version is version 3.0, dated April 3, 2023.

Chest X-rays and Lung Ultrasound Are Not Interchangeable in Intensive Care Practice

PURPOSE

Data comparing lung ultrasound (LUS) and chest X-rays (CXRs) have increased over the past years. However, there still is a lack of knowledge as to how these modalities compare with one another in the critical care setting, and several factors, including artificial study conditions, limit the generalizability of most published studies. Our study aimed to analyze the performance of LUS in comparison with CXRs in real-world critical care practice.

MATERIALS AND METHODS

This study presents new data from the prospective FASP-ICU trial. A total of 209 corresponding datasets of LUS and CXR results from 111 consecutive surgical ICU patients were subanalyzed, and categorial findings were compared. Statistical analysis was performed on the rates of agreement between the different imaging modalities.

RESULTS

A total of 1162 lung abnormalities were detected by LUS in ICU patients compared with 1228 detected by CXR, a non-significant difference (p = 0.276; 95% CI -0.886 to 0.254). However, the agreement rates varied between the observed abnormalities: the rate of agreement for the presence of interstitial syndrome ranged from 0 to 15%, consolidation from 0 to 56%, basal atelectasis from 33.9 to 49.34%, pleural effusion from 40.65 to 50%, and compression atelectasis from 14.29 to 19.3%. The rate of agreement was 0% for pneumothorax and 20.95% for hypervolemia.

CONCLUSIONS

LUS does not detect more lung abnormalities in real-world critical care practice than CXRs, although a higher sensitivity of LUS has been reported in previous studies. Overall, low agreement rates between LUS and CXRs suggest that these diagnostic techniques are not equivalent but instead are complementary and should be used alongside each other.

Ten Issues to Update in Nosocomial or Hospital-Acquired Pneumonia: An Expert Review

Nosocomial pneumonia, or hospital-acquired pneumonia (HAP), and ventilator-associated pneumonia (VAP) are important health problems worldwide, with both being associated with substantial morbidity and mortality. HAP is currently the main cause of death from nosocomial infection in critically ill patients. Although guidelines for the approach to this infection model are widely implemented in international health systems and clinical teams, information continually emerges that generates debate or requires updating in its management. This scientific manuscript, written by a multidisciplinary team of specialists, reviews the most important issues in the approach to this important infectious respiratory syndrome, and it updates various topics, such as a renewed etiological perspective for updating the use of new molecular platforms or imaging techniques, including the microbiological diagnostic stewardship in different clinical settings and using appropriate rapid techniques on invasive respiratory specimens. It also reviews both Intensive Care Unit admission criteria and those of clinical stability to discharge, as well as those of therapeutic failure and rescue treatment options. An update on antibiotic therapy in the context of bacterial multiresistance, in aerosol inhaled treatment options, oxygen therapy, or ventilatory support, is presented. It also analyzes the out-of-hospital management of nosocomial pneumonia requiring complete antibiotic therapy externally on an outpatient basis, as well as the main factors for readmission and an approach to management in the emergency department. Finally, the main strategies for prevention and prophylactic measures, many of them still controversial, on fragile and vulnerable hosts are reviewed.

Ultrasound imaging of lung disease and its relationship to histopathology: An experimentally validated simulation approach

Lung ultrasound (LUS) is a widely used technique in clinical lung assessment, yet the relationship between LUS images and the underlying disease remains poorly understood due in part to the complexity of the wave propagation physics in complex tissue/air structures. Establishing a clear link between visual patterns in ultrasound images and underlying lung anatomy could improve the diagnostic accuracy and clinical deployment of LUS. Reverberation that occurs at the lung interface is complex, resulting in images that require interpretation of the artifacts deep in the lungs. These images are not accurate spatial representations of the anatomy due to the almost total reflectivity and high impedance mismatch between aerated lung and chest wall. Here, we develop an approach based on the first principles of wave propagation physics in highly realistic maps of the human chest wall and lung to unveil a relationship between lung disease, tissue structure, and its resulting effects on ultrasound images. It is shown that Fullwave numerical simulations of ultrasound propagation and histology-derived acoustical maps model the multiple scattering physics at the lung interface and reproduce LUS B-mode images that are comparable to clinical images. However, unlike clinical imaging, the underlying tissue structure model is known and controllable. The amount of fluid and connective tissue components in the lung were gradually modified to model disease progression, and the resulting changes in B-mode images and non-imaging reverberation measures were analyzed to explain the relationship between pathological modifications of lung tissue and observed LUS.

Fully Automatic Dual-Probe Lung Ultrasound Scanning Robot for Screening Triage

Two-dimensional lung ultrasound (LUS) has widely emerged as a rapid and noninvasive imaging tool for the detection and diagnosis of coronavirus disease 2019 (COVID-19). However, image differences will be magnified due to changes in ultrasound (US) imaging experience, such as US probe attitude control and force control, which will directly affect the diagnosis results. In addition, the risk of virus transmission between sonographer and patients is increased due to frequent physical contact. In this study, a fully automatic dual-probe US scanning robot for the acquisition of LUS images is proposed and developed. Furthermore, the trajectory was optimized based on the velocity look-ahead strategy, the stability of contact force of the system and the scanning efficiency were improved by 24.13% and 29.46%, respectively. Also, the control ability of the contact force of robotic automatic scanning was 34.14 times higher than that of traditional manual scanning, which significantly improves the smoothness of scanning. Importantly, there was no significant difference in image quality obtained by robotic automatic scanning and manual scanning. Furthermore, the scanning time for a single person is less than 4 min, which greatly improves the efficiency of screening triage of group COVID-19 diagnosis and suspected patients and reduces the risk of virus exposure and spread.

Diagnostic Test Accuracy of Lung Ultrasound for Acute Chest Syndrome in Sickle Cell Disease: A Systematic Review and Meta-analysis

BACKGROUND

Acute chest syndrome (ACS) is a leading cause of death in patients with sickle cell disease. Lung ultrasound (LUS) is emerging as a point-of-care method to diagnose ACS, allowing for more rapid diagnosis in the ED setting and sparing patients from ionizing radiation exposure.

RESEARCH QUESTION

What is the diagnostic accuracy of LUS for ACS diagnosis, using the current reference standard of chest radiography?

STUDY DESIGN AND METHODS

Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed for this systematic review and meta-analysis. Embase, MEDLINE, Web of Science, and Google Scholar were used to compile all relevant studies. Two reviewers screened the studies for inclusion in this review. Cases of discrepancy were resolved by a third reviewer. Meta-analyses were conducted using both metadta and midas STATA software packages to retrieve summary receiver operating characteristic curves, sensitivities, and specificities. Three reviewers scored the studies with QUADAS-2 for risk of bias assessment.

RESULTS

From a total of 713 unique studies retrieved, six studies were included in the final quantitative synthesis. Of these, five studies were in pediatric EDs. Two studies were conference abstracts and not published manuscripts. Data were available for 625 possible ACS cases (97% of cases in patients aged ≤ 21 years) and 95 confirmed ACS diagnoses (pretest probability of 15.2%). The summary sensitivity was 0.92 (95% CI, 0.68-0.98) and the summary specificity was 0.89 (95% CI, 0.69-0.97) with an area under the curve of the summary receiver operating characteristic curve of 0.96 (95% CI, 0.94-0.97).

INTERPRETATION

LUS has excellent sensitivity and very good specificity for ACS diagnosis and may serve as an initial point-of-care test to facilitate rapid treatment of ACS and spare pediatric patients from ionizing radiation; however, further research is warranted to improve the generalizability to the adult sickle cell disease population.

Diagnostic Performance of Point of Care Ultrasound Compared to Chest X-Ray in Patients with Hypoxia at a Teaching Hospital Emergency Department in Uganda

Background

Hypoxia is a common presentation in the Emergency Department (ED) worldwide. It affects 9-12% of hospitalized adults in Sub-Saharan Africa. Timely diagnosis of the multiple causes such as pneumonia, heart failure among others is challenging. Chest X-Ray (CXR), one of the most utilized imaging modalities has many limitations, and the gold standard (Computed Tomography scan) is inaccessible. Point of care ultrasound (PoCUS) is more available and increasingly being used, however little is known of its performance in resource limited EDs. The study aimed to assess the diagnostic performance of PoCUS compared with CXR in identifying the causes of hypoxia in the medical ED.

Methods

49 adults presenting with hypoxia (SP02 ≤ 88%) in the medical ED were evaluated. Ultrasound of the lungs and heart (PoCUS) was done, then CXR obtained. Lung ultrasound (LUS) was compared with CXR (first reference standard). Chest X-Ray and PoCUS were each compared to the physician diagnosis (second reference standard) to determine agreement using an acceptable disagreement cut-off of 15%.

Results

31% more abnormalities were identified by LUS than CXR. Lung ultrasound findings agreed with CXR in 86% of the participants with moderate reliability (ĸ=0.75). There was no significant difference between the actual findings of the two tests (X²= 2, p 0.1). Using the second reference, 82% of the CXRs were similar with weak reliability (ĸ=0.5) compared to 98% of PoCUS findings with strong reliability (ĸ=0.9). Compared to PoCUS, CXRs significantly differed from the physician diagnosis (X²= 0.85, p 0.38 vs X²= 8.5, p 0.004 respectively).

Conclusion

Overall, PoCUS was not inferior to CXR when compared to final physician diagnosis in identifying causes of hypoxia, and LUS and CXR had comparable performance. Significantly more abnormalities were identified on PoCUS and it demonstrated better agreement and strong reliability with the physician diagnosis than CXR. We recommend PoCUS use in patients with hypoxia attending resource limited in- and pre-hospital settings.

Point-of-care ultrasound in diagnosis and management of congestive nephropathy

Congestive nephropathy is kidney dysfunction caused by the impact of elevated venous pressures on renal hemodynamics. As a part of cardiorenal syndrome, the diagnosis is usually made based on history and physical examination, with findings such as jugular venous distension, a third heart sound, and vital signs as supporting findings. More recently, however, these once though objective measures have come under scrutiny for their accuracy. At the same time, bedside ultrasound has increased in popularity and is routinely being used by clinicians to take some of the guess work out of making the diagnosis of volume overload and venous congestion. In this mini-review, we will discuss some of the traditional methods used to measure venous congestion, describe the role of point-of-care ultrasound and how it can ameliorate a clinician’s evaluation, and offer a description of venous excess ultrasound score, a relatively novel scoring technique used to objectively quantify congestion. While there is a paucity of published large scale clinical trials evaluating the potential benefit of ultrasonography in venous congestion compared to gold standard invasive measurements, more study is underway to solidify the role of this objective measure in daily clinical practice.

Point-of-care ultrasound needs assessment in a paediatric acute care setting in Malawi

OBJECTIVE

To describe the use of point-of-care ultrasound (POCUS) in an acute-care paediatric setting in Malawi, including clinical indications, types of examinations and frequency of positive findings.

METHODS

Retrospective, cross-sectional study of a convenience sample of POCUS examinations performed in one tertiary referral hospital in Lilongwe, Malawi over 1 year. POCUS examinations were performed by Paediatric Emergency Medicine physician consultants as part of routine clinical practice and at the request of local clinicians. Images were saved along with the clinical indication and physician interpretation for quality review. Ultrasounds performed by the radiology department and those examinations that were technically faulty, missing clinical application or interpretation were excluded.

RESULTS

In total, 225 ultrasounds of 142 patients were analysed. The most common clinical indications for which examinations were completed were respiratory distress (23%), oedema (11.7%) and shock/arrest (6.2%). The most common examinations performed were cardiac (41.8%) and lung (15.1%), focused assessment with sonography in trauma (FAST; 12.9%) and ultrasound-guided procedural examinations (9.8%). Pathology was identified in 68% of non-procedural examinations. Cardiac examinations demonstrated significant pathology, including reduced cardiac function (12.8%), gross cardiac structural abnormality (11.8%) and pericardial effusion (10.3%).

CONCLUSIONS

POCUS was used for both clinical decision-making and procedural guidance, and a significant number of POCUS examinations yielded positive findings. Thus, we propose that cardiopulmonary, FAST and procedural examinations should be considered in future for the POCUS curriculum in this setting.

Lung ultrasound (LUS) in pulmonary tuberculosis: correlation with chest CT and X-ray findings

AIMS

The aim is to describe lung ultrasound (LUS) findings in a cohort of patients with suspected pulmonary tuberculosis (PTB) and compare them with computed tomography (CT) and chest x-ray (CXR) findings in order to evaluate the potentiality of LUS in TB diagnosis.

METHODS

In this prospective study, 82 subjects with suspected TB were enrolled after being evaluated with CXR and chest CT. LUS was performed by blinded radiologists within 3 days after admission. A semiquantitative index was used: score 1 (lesions that extend for about 1-15% of the affected zone), score 2 (15-40%) and score 3 (40-100%).

RESULTS

Microbiological analysis confirmed TB diagnosis in 58/82 (70.7%). CT was positive in all patients, LUS in 79/82 (96.3%) CXR in 78/82 (95.1%) and adding LUS and CXR in 100%. In PTB patients we found a great number of lungs zones with micronodules and with total findings than non-TPB patients (p < 0.05). Overall LUS sensitivity was 80%, greater for micronodules (82%) and nodules (95%), lower for consolidation with air bronchogram (72%) and cavitations (33%). We reported 5 complicated pleural effusion at LUS, only 1 in CT. CXR overall sensitivity was 81%. Adding CXR and LUS findings we reported a sensitivity of 90%.

CONCLUSIONS

LUS could be considered a valid, non-invasive and cost-effective diagnostic tool especially in world regions where CT were not available, also in addiction with CXR.

TRIAL REGISTRATION

This study was approved by the Ethics Committee of our University Hospital (rif. CEAVC 14,816).

Ultra-low dose computed tomography of the chest in an emergency setting: A prospective agreement study

Ultra-low dose computed tomography (ULD-CT) assessed by non-radiologists in a medical Emergency Department (ED) has not been examined in previous studies. To (i) investigate intragroup agreement among attending physicians caring for ED patients (i.e., radiologists, senior- and junior clinicians) and medical students for the detection of acute lung conditions on ULD-CT and supine chest X-ray (sCXR), and (ii) evaluate the accuracy of interpretation compared to the reference standard. In this prospective study, non-traumatic patients presenting to the ED, who received an sCXR were included. Between February and July 2019, 91 patients who underwent 93 consecutive examinations were enrolled. Subsequently, a ULD-CT and non-contrast CT were performed. The ULD-CT and sCXR were assessed by 3 radiologists, 3 senior clinicians, 3 junior clinicians, and 3 medical students for pneumonia, pneumothorax, pleural effusion, and pulmonary edema. The non-contrast CT, assessed by a chest radiologist, was used as the reference standard. The results of the assessments were compared within each group (intragroup agreement) and with the reference standard (accuracy) using kappa statistics. Accuracy and intragroup agreement improved for pneumothorax on ULD-CT compared with the sCXR for all groups. Accuracy and intragroup agreement improved for pneumonia on ULD-CT when assessed by radiologists and for pleural effusion when assessed by medical students. In patients with acute lung conditions ULD-CT offers improvement in the detection of pneumonia by radiologists and the detection of pneumothorax by radiologists as well as non-radiologists compared to sCXR. Therefore, ULD-CT may be considered as an alternative first-line imaging modality to sCXR for non-traumatic patients who present to EDs.

Point-of-care contrast enhanced lung ultrasound and COVID-19

OBJECTIVES

Bedside lung ultrasound has been indispensable during the coronavirus disease 2019 (COVID-19) pandemic, allowing us to rapidly assess critically unwell patients. We demonstrate the unique application of contrast-enhanced ultrasound with the aim of further understanding this disease.

METHODS

Patient demographics were recorded alongside recent cross-sectional imaging and inflammatory markers. Ultrasound was conducted by experienced operators in a portable setting. Conventional six-point lung ultrasound method was used to evaluate B-lines, small (subpleural) consolidation and the pleura. Areas of small consolidation were targeted after intravenous administration of ultrasound contrast.

RESULTS

The areas of small consolidations, a potential sign of pneumonia on B-mode lung ultrasound, usually enhance on contrast-enhanced ultrasound. Our study revealed these areas to be avascular, indicating an underlying thrombotic/infarction process. Findings were present in 100% of the patients we examined. We have also shown that the degree of infarction correlates with CT severity (r = 0.4) and inflammatory markers, and that these areas improve as patients recover.

CONCLUSIONS

We confirmed the theory of immune thrombus by identifying the presence of microthrombi in the lungs of 100% of our patients, despite 79% having had a recent negative CT pulmonary angiogram study. contrast-enhanced ultrasound can be utilised to add confidence to an uncertain COVID-19 diagnosis and for prognosticating and monitoring progress in confirmed COVID-19 patients. Contrast-enhanced ultrasound is clearly very different to CT, the gold standard, and while there are specific pathologies that can only be detected on CT, contrast-enhanced ultrasound has many advantages, most notability the ability to pick up microthrombi at the periphery of the lungs.

Seeing is Believing: The Import of Lung Ultrasound!

How to cite this article: Govil D, Pachisia AV. Seeing is Believing: The Import of Lung Ultrasound! Indian J Crit Care Med 2022;26(8):894–895.

Molecular imaging as a tool for evaluation of COVID-19 sequelae – A review of literature

Coronavirus disease 2019 (COVID-19) is caused by the novel viral pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 primarily involves the lungs. Nucleic acid testing based on reverse-transcription polymerase chain reaction of respiratory samples is the current gold standard for the diagnosis of SARS-CoV-2 infection. Imaging modalities have an established role in triaging, diagnosis, evaluation of disease severity, monitoring disease progression, extra-pulmonary involvement, and complications. As our understanding of the disease improves, there has been substantial evidence to highlight its potential for multi-systemic involvement and development of long-term sequelae. Molecular imaging techniques are highly sensitive, allowing non-invasive visualization of physiological or pathological processes at a cellular or molecular level with potential for detection of functional changes earlier than conventional radiological imaging. The purpose of this review article is to highlight the evolving role of molecular imaging in evaluation of COVID-19 sequelae. Though not ideal for diagnosis, the various modalities of molecular imaging play an important role in assessing pulmonary and extra-pulmonary sequelae of COVID-19. Perfusion imaging using single photon emission computed tomography fused with computed tomography (CT) can be utilized as a first-line imaging modality for COVID-19 related pulmonary embolism. ¹⁸F-fluorodeoxyglucose positron emission tomography (PET)/CT is a sensitive tool to detect multi-systemic inflammation, including myocardial and vascular inflammation. PET in conjunction with magnetic resonance imaging helps in better characterization of neurological sequelae of COVID-19. Despite the fact that the majority of published literature is retrospective in nature with limited sample sizes, it is clear that molecular imaging provides additional valuable information (complimentary to anatomical imaging) with semi-quantitative or quantitative parameters to define inflammatory burden and can be used to guide therapeutic strategies and assess response. However, widespread clinical applicability remains a challenge owing to longer image acquisition times and the need for adoption of infection control protocols.

The Accuracy of Lung Ultrasonography Diagnosis of Community-Acquired Pneumonia, in an Adult Cohort

Objective:

Community-acquired pneumonia (CAP) is a common respiratory infection, and diagnosis is frequently performed using a chest radiography (CXR). Sonography is an available method with less radiation exposure, but has not been confirmed for diagnosis of CAP. The objective was to compare the diagnostic accuracy of sonography.

Materials and Methods:

In this cross-sectional study, 90 adult patients (aged >18 years) were admitted to the emergency department of two university-affiliated hospitals in Southwest Iran, from July to December 2019, with a confirmed diagnosis of CAP. The patient symptoms and CXR results were included as part of this study. Within 24 hours after obtaining a CXR, a lung ultrasonogram (LUS) was performed. The diagnostic accuracy of semiquantitative LUS (SQLUS) was compared with CXR results using the Pearson chi-square test and Fisher’s exact test.

Results:

The mean age of participants was 52.98 ± 16.77 years. 51 were men (56.7%). 28 patients (31.1%), who had abnormal SQLUS results, were not associated with CXR findings ( P = .296). SQLUS showed poor diagnostic accuracy for LUS (31.11%).

Conclusion:

This study results could not confirm LUS as an accurate method for diagnosing CAP in adult patients; although due to the convenient sample of adults and clinical-based diagnosis of CAP, any generalization of the results should be made with caution.

LOw-dose CT Or Lung UltraSonography versus standard of care based-strategies for the diagnosis of pneumonia in the elderly: protocol for a multicentre randomised controlled trial (OCTOPLUS)

INTRODUCTION

Pneumonia is a leading cause of mortality and a common indication for antibiotic in elderly patients. However, its diagnosis is often inaccurate. We aim to compare the diagnostic accuracy, the clinical and cost outcomes and the use of antibiotics associated with three imaging strategies in patients >65 years old with suspected pneumonia in the emergency room (ER): chest X-ray (CXR, standard of care), low-dose CT scan (LDCT) or lung ultrasonography (LUS).

METHODS AND ANALYSIS

This is a multicentre randomised superiority clinical trial with three parallel arms. Patients will be allocated in the ER to a diagnostic strategy based on either CXR, LDCT or LUS. All three imaging modalities will be performed but the results of two of them will be masked during 5 days to the patients, the physicians in charge of the patients and the investigators according to random allocation. The primary objective is to compare the accuracy of LDCT versus CXR-based strategies. As secondary objectives, antibiotics prescription, clinical and cost outcomes will be compared, and the same analyses repeated to compare the LUS and CXR strategies. The reference diagnosis will be established a posteriori by a panel of experts. Based on a previous study, we expect an improvement of 16% of the accuracy of pneumonia diagnosis using LDCT instead of CXR. Under this assumption, and accounting for 10% of drop-out, the enrolment of 495 patients is needed to prove the superiority of LDCT over CRX (alpha error=0.05, beta error=0.10).

ETHICS AND DISSEMINATION

Ethical approval: CER Geneva 2019-01288.

TRIAL REGISTRATION NUMBER

NCT04978116.

Applications of artificial intelligence in lung ultrasound: Review of deep learning methods for COVID-19 fighting

BACKGROUND

The pandemic outbreak of the novel coronavirus disease (COVID-19) has highlighted the need to combine rapid, non-invasive and widely accessible techniques with the least risk of patient’s cross-infection to achieve a successful early detection and surveillance of the disease. In this regard, the lung ultrasound (LUS) technique has been proved invaluable in both the differential diagnosis and the follow-up of COVID-19 patients, and its potential may be destined to evolve. Recently, indeed, LUS has been empowered through the development of automated image processing techniques.

AIM

To provide a systematic review of the application of artificial intelligence (AI) technology in medical LUS analysis of COVID-19 patients using the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines.

METHODS

A literature search was performed for relevant studies published from March 2020 - outbreak of the pandemic - to 30 September 2021. Seventeen articles were included in the result synthesis of this paper.

RESULTS

As part of the review, we presented the main characteristics related to AI techniques, in particular deep learning (DL), adopted in the selected articles. A survey was carried out on the type of architectures used, availability of the source code, network weights and open access datasets, use of data augmentation, use of the transfer learning strategy, type of input data and training/test datasets, and explainability.

CONCLUSION

Finally, this review highlighted the existing challenges, including the lack of large datasets of reliable COVID-19-based LUS images to test the effectiveness of DL methods and the ethical/regulatory issues associated with the adoption of automated systems in real clinical scenarios.

Potential for Use of Portable Ultrasound Devices in Rural and Remote Settings in Australia and Other Developed Countries: A Systematic Review

Introduction

Objective

Methods

Results

Conclusion

Lung and airway ultrasound in pediatric anesthesia

Perioperative lung ultrasound is a continuously evolving modality with numerous applications for the pediatric anesthesiologist. Lung ultrasound can be used at the bedside, including intraoperatively, to augment traditional physical examination methods of assessing cardiopulmonary structures and identifying the presence of specific and clinically significant pathology. With regard to the lungs, ultrasound has been shown to be highly sensitive at identification of pulmonary pathologies, particularly those of interest in the acute care setting (eg, pleural effusion, pneumothorax). With its relative ease of performance, lung ultrasound should be considered in the initial evaluation of intraoperative hypoxemia particularly when traditional modes of evaluation are nonexplanatory. This educational review introduces the basic concepts of lung ultrasound as they relate to pediatric anesthesia patients.

Artificial intelligence for COVID-19: battling the pandemic with computational intelligence

The new coronavirus disease 2019 (COVID-19) has become a global pandemic leading to over 180 million confirmed cases and nearly 4 million deaths until June 2021, according to the World Health Organization. Since the initial report in December 2019 , COVID-19 has demonstrated a high transmission rate (with an R₀ > 2), a diverse set of clinical characteristics (e.g., high rate of hospital and intensive care unit admission rates, multi-organ dysfunction for critically ill patients due to hyperinflammation, thrombosis, etc.), and a tremendous burden on health care systems around the world. To understand the serious and complex diseases and develop effective control, treatment, and prevention strategies, researchers from different disciplines have been making significant efforts from different aspects including epidemiology and public health, biology and genomic medicine, as well as clinical care and patient management. In recent years, artificial intelligence (AI) has been introduced into the healthcare field to aid clinical decision-making for disease diagnosis and treatment such as detecting cancer based on medical images, and has achieved superior performance in multiple data-rich application scenarios. In the COVID-19 pandemic, AI techniques have also been used as a powerful tool to overcome the complex diseases. In this context, the goal of this study is to review existing studies on applications of AI techniques in combating the COVID-19 pandemic. Specifically, these efforts can be grouped into the fields of epidemiology, therapeutics, clinical research, social and behavioral studies and are summarized. Potential challenges, directions, and open questions are discussed accordingly, which may provide new insights into addressing the COVID-19 pandemic and would be helpful for researchers to explore more related topics in the post-pandemic era.

Paediatric emergency care at an academic referral hospital in Mozambique

Background

Improved emergency care of children with acute illness or injuries is needed for countries in Africa to continue to reduce childhood mortality rates. Quality improvement efforts will depend on robust baseline data, but little has been published on the breadth and severity of paediatric illness seen in Mozambique.

Methods

This was a retrospective review of routinely collected provider shift summary data from the Paediatric Emergency Department (PED) at Hospital Central de Maputo (HCM), the principal academic and referral hospital in the country. All children 0–14 years of age seen in the 12-month period from August 2018–July 2019 were included. Descriptive statistical analyses were performed.

Results

Data from 346 days and 64,966 patient encounters were analyzed. The large majority of patients (96.4%) presented directly to the PED without referral from a lower level facility. An average of 188 patients was seen per day, with significant seasonal variation peaking in March (292 patients/day). The most common diagnoses were upper respiratory infections (URI), gastroenteritis, asthma, and dermatologic problems. The highest acuity diagnoses were neurologic problems (59%), asthma (57%), and neonatal diagnoses (50%). Diagnoses with the largest proportion of admissions included neurologic problems, malaria, and neonatal diagnoses. Rapid malaria antigen tests were the most commonly ordered laboratory test across all diagnostic categories; full blood count (FBC) and chemistries were also commonly ordered. Urinalysis and HIV testing were rarely done in the PED.

Conclusion

This epidemiologic profile of illness seen in the HCM PED will allow for improved resource utilisation. We identified opportunities for evidence-based care algorithms for common diagnoses such as respiratory illness to improve patient care and flow. The PED may also be able to optimize laboratory and radiology evaluation for patients and develop standardized admission criteria by diagnosis.

SpecMEn-DL: spectral mask enhancement with deep learning models to predict COVID-19 from lung ultrasound videos

Lung Ultrasound (LUS) images are considered to be effective for detecting Coronavirus Disease (COVID-19) as an alternative to the existing reverse transcription-polymerase chain reaction (RT-PCR)-based detection scheme. However, the recent literature exhibits a shortage of works dealing with LUS image-based COVID-19 detection. In this paper, a spectral mask enhancement (SpecMEn) scheme is introduced along with a histogram equalization pre-processing stage to reduce the noise effect in LUS images prior to utilizing them for feature extraction. In order to detect the COVID-19 cases, we propose to utilize the SpecMEn pre-processed LUS images in the deep learning (DL) models (namely the SpecMEn-DL method), which offers a better representation of some characteristics features in LUS images and results in very satisfactory classification performance. The performance of the proposed SpecMEn-DL technique is appraised by implementing some state-of-the-art DL models and comparing the results with related studies. It is found that the use of the SpecMEn scheme in DL techniques offers an average increase in accuracy and F 1 score of 11 % and 11.75 % , respectively, at the video-level. Comprehensive analysis and visualization of the intermediate steps manifest a very satisfactory detection performance creating a flexible and safe alternative option for the clinicians to get assistance while obtaining the immediate evaluation of the patients.

Lung ultrasound volume sweep imaging for respiratory illness: a new horizon in expanding imaging access

Background

Respiratory illness is a leading cause of morbidity in adults and the number one cause of mortality in children, yet billions of people lack access to medical imaging to assist in its diagnosis. Although ultrasound is highly sensitive and specific for respiratory illness such as pneumonia, its deployment is limited by a lack of sonographers. As a solution, we tested a standardised lung ultrasound volume sweep imaging (VSI) protocol based solely on external body landmarks performed by individuals without prior ultrasound experience after brief training. Each step in the VSI protocol is saved as a video clip for later interpretation by a specialist.

Methods

Dyspneic hospitalised patients were scanned by ultrasound naive operators after 2 hours of training using the lung ultrasound VSI protocol. Separate blinded readers interpreted both lung ultrasound VSI examinations and standard of care chest radiographs to ascertain the diagnostic value of lung VSI considering chest X-ray as the reference standard. Comparison to clinical diagnosis as documented in the medical record and CT (when available) were also performed. Readers offered a final interpretation of normal, abnormal, or indeterminate/borderline for each VSI examination, chest X-ray, and CT.

Results

Operators scanned 102 subjects (0–89 years old) for analysis. Lung VSI showed a sensitivity of 93% and a specificity of 91% for an abnormal chest X-ray and a sensitivity of 100% and a specificity of 93% for a clinical diagnosis of pneumonia. When any cases with an indeterminate rating on chest X-ray or ultrasound were excluded (n=38), VSI lung ultrasound showed 92% agreement with chest X-ray (Cohen’s κ 0.83 (0.68 to 0.97, p<0.0001)). Among cases with CT (n=21), when any ultrasound with an indeterminate rating was excluded (n=3), there was 100% agreement with VSI.

Conclusion

Lung VSI performed by previously inexperienced ultrasound operators after brief training showed excellent agreement with chest X-ray and high sensitivity and specificity for a clinical diagnosis of pneumonia. Blinded readers were able to identify other respiratory diseases including pulmonary oedema and pleural effusion. Deployment of lung VSI could benefit the health of the global community.

Diagnostic ultrasound imaging of the lung: A simulation approach based on propagation and reverberation in the human body

Although ultrasound cannot penetrate a tissue/air interface, it images the lung with high diagnostic accuracy. Lung ultrasound imaging relies on the interpretation of "artifacts," which arise from the complex reverberation physics occurring at the lung surface but appear deep inside the lung. This physics is more complex and less understood than conventional B-mode imaging in which the signal directly reflected by the target is used to generate an image. Here, to establish a more direct relationship between the underlying acoustics and lung imaging, simulations are used. The simulations model ultrasound propagation and reverberation in the human abdomen and at the tissue/air interfaces of the lung in a way that allows for direct measurements of acoustic pressure inside the human body and various anatomical structures, something that is not feasible clinically or experimentally. It is shown that the B-mode images beamformed from these acoustical simulations reproduce primary clinical features that are used in diagnostic lung imaging, i.e., A-lines and B-lines, with a clear relationship to known underlying anatomical structures. Both the oblique and parasagittal views are successfully modeled with the latter producing the characteristic "bat sign," arising from the ribs and intercostal part of the pleura. These simulations also establish a quantitative link between the percentage of fluid in exudative regions and the appearance of B-lines, suggesting that the B-mode may be used as a quantitative imaging modality.

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.

Pulmonary COVID-19: Learning Spatiotemporal Features Combining CNN and LSTM Networks for Lung Ultrasound Video Classification

Deep Learning is a very active and important area for building Computer-Aided Diagnosis (CAD) applications. This work aims to present a hybrid model to classify lung ultrasound (LUS) videos captured by convex transducers to diagnose COVID-19. A Convolutional Neural Network (CNN) performed the extraction of spatial features, and the temporal dependence was learned using a Long Short-Term Memory (LSTM). Different types of convolutional architectures were used for feature extraction. The hybrid model (CNN-LSTM) hyperparameters were optimized using the Optuna framework. The best hybrid model was composed of an Xception pre-trained on ImageNet and an LSTM containing 512 units, configured with a dropout rate of 0.4, two fully connected layers containing 1024 neurons each, and a sequence of 20 frames in the input layer (20×2018). The model presented an average accuracy of 93% and sensitivity of 97% for COVID-19, outperforming models based purely on spatial approaches. Furthermore, feature extraction using transfer learning with models pre-trained on ImageNet provided comparable results to models pre-trained on LUS images. The results corroborate with other studies showing that this model for LUS classification can be an important tool in the fight against COVID-19 and other lung diseases.

COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images

COVID-19 or related viral pandemics should be detected and managed without hesitation, since the virus spreads very rapidly. Often with insufficient human and electronic resources, patients need to be checked from stable patients using vital signs, radiographic photographs, or ultrasound images. Vital signs do not often offer the right outcome, and radiographic photos have a variety of other problems. Lung ultrasound (LUS) images can provide good screening without a lot of complications. This paper suggests a model of a convolutionary neural network (CNN) that has fewer learning parameters but can achieve strong accuracy. The model has five main blocks or layers of convolution connectors. A multi-layer fusion functionality of each block is proposed to improve the efficiency of the COVID-19 screening method utilizing the proposed model. Experiments are conducted using freely accessible LUS photographs and video datasets. The proposed fusion method has 92.5% precision, 91.8% accuracy, and 93.2% retrieval using the data collection. These efficiency metric levels are considerably higher than those used in any of the state-of-the-art CNN versions.

A simple lung ultrasound protocol for the screening of COVID-19 pneumonia in the emergency department

The most relevant manifestation of coronavirus disease 2019 (COVID-19) is interstitial pneumonia. Several lung ultrasound (US) protocols for pneumonia diagnosis are used in clinical practice, but none has been proposed for COVID-19 patients' screening in the emergency department. We adopted a simplified 6-scan lung US protocol for COVID-19 pneumonia diagnosis (LUSCOP) and compared its sensitivity with high resolution computed tomography (HRCT) in patients suspected for COVID-19, presenting to one Emergency Department from February 21st to March 15th, 2020, during the outbreak burst in northern Italy. Patients were retrospectively enrolled if both LUSCOP protocol and HRCT were performed in the Emergency Department. The sensitivity of LUSCOP protocol and HRCT were compared. COVID-19 pneumonia's final diagnosis was based on real-time reverse-transcription polymerase chain reaction from nasal-pharyngeal swab and on clinical data. Out of 150 suspected COVID-19 patients, 131 were included in the study, and 130 had a final diagnosis of COVID-19 pneumonia. The most frequent lung ultrasonographic features were: bilateral B-pattern in 101 patients (77%), B-pattern with subpleural consolidations in 26 (19.8%) and lung consolidations in 2 (1.5%). LUSCOP Protocol was consistent with HRCT in correctly screening 130 out of the 131 COVID-19 pneumonia cases (99.2%). In one case COVID-19 pneumonia was excluded by both HRCT and lung US. LUSCOP protocol showed optimal sensitivity and can be proposed as a simple screening tool for COVID-19 pneumonia diagnosis in the context of outbreak burst areas where prompt isolation of suspected patients is crucial for patients' and operators' safety.

Tele-Operative Low-Cost Robotic Lung Ultrasound Scanning Platform for Triage of COVID-19 Patients

Novel severe acute respiratory syndrome coronavirus 2 (COVID-19) has become a pandemic of epic proportions, and global response to prepare health systems worldwide is of utmost importance. 2-dimensional (2D) lung ultrasound (LUS) has emerged as a rapid, noninvasive imaging tool for diagnosing COVID-19 infected patients. Concerns surrounding LUS include the disparity of infected patients and healthcare providers, and importantly, the requirement for substantial physical contact between the patient and operator, increasing the risk of transmission. New variants of COVID-19 will continue to emerge; therefore, mitigation of the virus's spread is of paramount importance. A tele-operative robotic ultrasound platform capable of performing LUS in COVID-19 infected patients may be of significant benefit, especially in low- and middle-income countries. The authors address the issues mentioned above surrounding the use of LUS in COVID-19 infected patients and the potential for extension of this technology in a resource-limited environment. Additionally, first-time application, feasibility, and safety were validated in healthy subjects. Preliminary results demonstrate that our platform allows for the successful acquisition and application of robotic LUS in humans.

Correlation of Oxygenation and Radiographic Assessment of Lung Edema (RALE) Score to Lung Ultrasound Score (LUS) in Acute Respiratory Distress Syndrome (ARDS) Patients in the Intensive Care Unit

Background

Lung ultrasound score (LUS) as well as radiographic assessment of lung edema (RALE) score as calculated from chest radiography (CXR) have been applied to assess Acute Respiratory Distress Syndrome (ARDS) severity. CXRs, which are frequently performed in ARDS patients, pose a greater risk of radiation exposure to patients and health care staff.

Aims and objectives

The aim of the study was to evaluate if LUS had a better correlation to oxygenation (PaO₂/FiO₂) compared with the RALE score in ARDS patients. We also aimed to analyse if there was a correlation between RALE score and LUS. We wanted to determine the LUS and RALE score cut-off, which could predict a prolonged length of intensive care unit (ICU) stay (≥10 days) and survival.

Methods

Thirty-seven patients aged above 18 years with ARDS as per Berlin definition and admitted to the ICU were included in the study. It was a retrospective study done over a period of 11 months. On the day of admission to ICU, the global and basal LUS, global and basal RALE score, and PaO₂ /FiO₂ were recorded. Outcome and days of ICU stay were noted.

Results

Global LUS score and PaO₂/FiO₂ showed the best negative correlation (r = –0.491), which was significant (p = 0.002), followed by global RALE score and PaO₂/FiO₂ (r = –0.422, p = 0.009). Basal LUS and PaO₂/FiO₂ also had moderate negative correlation (r = –0.334, p = 0.043) followed by basal RALE score and PaO₂/FiO₂ (r = –0.34, p = 0.039). Global RALE score and global LUS did not show a significant correlation. Similarly, there was no significant correlation between basal RALE score and basal LUS. Global and basal LUS as well as global and basal RALE score were not beneficial in predicting either a prolonged length of ICU stay or survival as the area under curve was low.

Conclusion

In ARDS patients, global LUS had the best correlation to oxygenation (PaO₂/FiO₂), followed by global RALE score. Basal LUS and basal RALE score also had moderate correlation to oxygenation. However, there was no significant correlation between global LUS and global RALE score as well as between basal LUS and basal RALE score. Global and basal LUS as well as global and basal RALE scores were not able to predict a prolonged ICU stay or survival in ARDS patients.

An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound

The COVID-19 pandemic has become one of the biggest threats to the global healthcare system, creating an unprecedented condition worldwide. The necessity of rapid diagnosis calls for alternative methods to predict the condition of the patient, for which disease severity estimation on the basis of Lung Ultrasound (LUS) can be a safe, radiation-free, flexible, and favorable option. In this paper, a frame-based 4-score disease severity prediction architecture is proposed with the integration of deep convolutional and recurrent neural networks to consider both spatial and temporal features of the LUS frames. The proposed convolutional neural network (CNN) architecture implements an autoencoder network and separable convolutional branches fused with a modified DenseNet-201 network to build a vigorous, noise-free classification model. A five-fold cross-validation scheme is performed to affirm the efficacy of the proposed network. In-depth result analysis shows a promising improvement in the classification performance by introducing the Long Short-Term Memory (LSTM) layers after the proposed CNN architecture by an average of 7-12%, which is approximately 17% more than the traditional DenseNet architecture alone. From an extensive analysis, it is found that the proposed end-to-end scheme is very effective in detecting COVID-19 severity scores from LUS images.

Diagnostic Performance of Ultrasonography in Patients With Pneumonia: An Updated Comparative Systematic Review and Meta-analysis

Objectives:

A chest radiograph (CXR) is still the preferred diagnostic method when pneumonia is suspected, although the sensitivity is relatively low. The aim of this study was to compare the diagnostic sensitivity, specificity, and accuracy of ultrasonography (US) for the diagnosis of community-acquired pneumonia (CAP), compared with CXR.

Materials and Methods:

A principled search was conducted to identify original English articles using PubMed, EMBASE, Web of Science, Scopus, and the Cochrane library, with the end date of October 2020. A combination of keywords, such as “ultrasound” or “ultrasonography,” “pneumonia,” “sensitivity,” and “specificity,” was used. Methodologic quality was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 criteria. Statistical analysis was completed on the resulting study data.

Results:

The search produced 16 eligible articles that reported on 2040 patients. The overall pooled sensitivity for US and CXR, to diagnose pneumonia, was 0.96 and 0.65, respectively. The overall pooled specificity for US and CXR was 0.85 and 0.81, respectively. The overall pooled positive likelihood ratio for US and CXR was 9.74 and 3.67, respectively. The negative likelihood ratio for US and CXR was 0.05 and 0.42, respectively. In addition, summary receiver operative characteristics areas under the curve were 0.98 for US and 0.77 for CXR.

Conclusion:

This review demonstrated that lung US is a useful technique for the diagnosis of pneumonia. This diagnostic method can be used by emergency physicians with high accuracy, sensitivity, and specificity. Among an elderly population, this diagnostic method may be a better choice than CXR. The rapid performance of lung US may facilitate a quick, cost-effective, and safe diagnosis of this potentially fatal disease.

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.

Point-of-care lung ultrasound for the assessment of pneumonia: a narrative review in the COVID-19 era

In the coronavirus disease-2019 (COVID-19) era, point-of-care lung ultrasound (LUS) has attracted increased attention. Prospective studies on LUS for the assessment of pneumonia in adult patients were extensively carried out for more than 10 years before this era. None of these prospective studies attempted to differentiate bacterial and viral pneumonia in adult patients using LUS. The majority of studies considered the LUS examination to be positive if sonographic consolidations or multiple B-lines were observed. Significant differences existed in the accuracy of these studies. Some studies revealed that LUS showed superior sensitivity to chest X-ray. These results indicate that point-of-care LUS has the potential to be an initial imaging modality for the diagnosis of pneumonia. The LUS diagnosis of ventilator-associated pneumonia in intensive care units is more challenging in comparison with the diagnosis of community-acquired pneumonia in emergency departments due to the limited access to the mechanically ventilated patients and the high prevalence of atelectasis. However, several studies have demonstrated that the combination of LUS findings with other clinical markers improved the diagnostic accuracy. In the COVID-19 era, many case reports and small observational studies on COVID-19 pneumonia have been published in a short period. Multiple B-lines were the most common and consistent finding in COVID-19 pneumonia. Serial LUS showed the deterioration of the disease. The knowledge and ideas on the application of LUS in the management of pneumonia that are expected to accumulate in the COVID-19 era may provide us with clues regarding more appropriate management.

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: Applications in Low- and Middle-Income Countries

Purpose of Review

This review highlights the applications of point-of-care ultrasound in low- and middle-income countries and shows the diversity of ultrasound in the diagnosis and management of patients.

Recent Findings

There is a paucity of data on point-of-care ultrasound in anesthesiology in low- and middle-income countries. However, research has shown that point-of-care ultrasound can effectively help manage infectious diseases, as well as abdominal and pulmonary pathologies.

Summary

Point-of-care ultrasound is a low-cost imaging modality that can be used for the diagnosis and management of diseases that affect low- and middle-income countries. There is limited data on the use of ultrasound in anesthesiology, which provides clinicians and researchers opportunity to study its use during the perioperative period.

Lung Ultrasound Findings Compared with Chest X-Ray Findings in Known Pulmonary Tuberculosis Patients: A Cross-Sectional Study in Lima, Peru

Lung ultrasound (LUS) is highly portable and has excellent diagnostic accuracy for pneumonia compared with conventional radiography, but the literature on its use in pulmonary tuberculosis (PTB) is limited. This study characterized LUS lesions in patients with PTB and compared them with chest X-ray (CXR) findings. Adult patients in Lima, Peru, with PTB were recruited within 1 week of starting antituberculosis treatment. Comprehensive LUS was performed in all patients at enrollment and assessed for consolidation, small subpleural consolidation (SPC, hypothesized to be a marker of CXR consolidation), cavity, pleural effusion, pathologic B-lines, and miliary pattern. Patient CXRs were digitized and interpreted by a board-certified radiologist. Fifty-one patients were included in the final analysis. Lung ultrasound detected either consolidation or SPC in 96.1% of participants. No significant difference was found between the LUS detection of a composite of consolidation or SPC, and CXR detection of consolidation (96.1% versus 98%, P > 0.99). The proportion of patients with cavity detected by LUS was significantly lower than that detected by CXR (5.9% versus 51%, P < 0.001). Overall, LUS detection of consolidation or SPC may be a sensitive marker for diagnosis of PTB. Lung ultrasound demonstrated poor ability to detect radiographically identified cavity, although previous studies suggest SPC could add specificity for the diagnosis of PTB. Based on its portability and evidence base for diagnosing other pulmonary diseases, LUS may have a role in screening and diagnosis of PTB in areas without ready access to CXR. Further studies should evaluate its diagnostic accuracy in patients with and without PTB.

The role of point of care ultrasound in radiology department: update and prospective. A statement of Italian college ultrasound

Ultrasound, in its new point-of-care conception, has been called the stethoscope of the future. Goal-directed bedside ultrasound examination, performed by a healthcare provider to answer a specific diagnostic question or guide an invasive procedure, is currently revolutionizing medical practice. It is used by various specialties in multiple clinical contexts for procedural, diagnostic, and screening applications. Point-of-care ultrasound is also a strategic technique in clinical radiology; it brings the radiologist closer to the patient (in interaction and understanding) like interventional and angiography procedures and, as an integrative imaging modality, is a vital radiological tool for decision-making in many situations. In this commentary, we present our observations on the use of ultrasound, in a sincere appeal to refrain from omitting ultrasound as a diagnostic technique in this era of deep professional change, in which radiologists must return to being a true clinical semiologist.

COVID-19 Control by Computer Vision Approaches: A Survey

The COVID-19 pandemic has triggered an urgent call to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of artificial intelligence, has enjoyed recent success in solving various complex problems in health care and has the potential to contribute to the fight of controlling COVID-19. In response to this call, computer vision researchers are putting their knowledge base at test to devise effective ways to counter COVID-19 challenge and serve the global community. New contributions are being shared with every passing day. It motivated us to review the recent work, collect information about available research resources, and an indication of future research directions. We want to make it possible for computer vision researchers to find existing and future research directions. This survey article presents a preliminary review of the literature on research community efforts against COVID-19 pandemic.

Recommendations for Lung Ultrasound in Internal Medicine

A growing amount of evidence prompts us to update the first version of recommendations for lung ultrasound in internal medicine (POLLUS-IM) that was published in 2018. The recommendations were established in several stages, consisting of: literature review, assessment of literature data quality (with the application of QUADAS, QUADAS-2 and GRADE criteria) and expert evaluation carried out consistently with the modified Delphi method (three rounds of on-line discussions, followed by a secret ballot by the panel of experts after each completed discussion). Publications to be analyzed were selected from the following databases: Pubmed, Medline, OVID, and Embase. New reports published as of October 2019 were added to the existing POLLUS-IM database used for the original publication of 2018. Altogether, 528 publications were systematically reviewed, including 253 new reports published between September 2017 and October 2019. The new recommendations concern the following conditions and issues: pneumonia, heart failure, monitoring dialyzed patients' hydration status, assessment of pleural effusion, pulmonary embolism and diaphragm function assessment. POLLUS-IM 2020 recommendations were established primarily for clinicians who utilize lung ultrasound in their everyday clinical work.

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound

Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, video-level, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.

Accuracy of lung ultrasonography in the hands of non-imaging specialists to diagnose and assess the severity of community-acquired pneumonia in adults: a systematic review

OBJECTIVES

We aimed to systematically review the published literature regarding adults with clinical suspicion of pneumonia that compares the accuracy of lung ultrasonography (LUS) performed by non-imaging specialists to other reference standards in diagnosing and evaluating the severity of community-acquired pneumonia. Moreover, we aimed to describe LUS training and the speciality of the physician performing LUS, time spent on the LUS procedure and potential harms to patients.

MATERIALS AND METHODS

We searched MEDLINE, Embase, CINAHL, Web of Science and Cochrane Central Register of Controlled Trials up until May 2019. We included studies that used LUS to diagnose pneumonia, but also confirmed pneumonia by other means. Publications were excluded if LUS was performed by a sonographer or radiologist (imaging specialists) or performed on other indications than suspicion of pneumonia. Two review authors screened and selected articles, extracted data and assessed quality using Quality Assessment of Diagnostic Accuracy Studies 2.

RESULTS

We included 17 studies. The sensitivity of LUS to diagnose pneumonia ranged from 0.68 to 1.00; however, in 14 studies, sensitivity was ≥0.91. Specificities varied from 0.57 to 1.00. We found no obvious differences between studies with low and high diagnostic accuracy. The non-imaging specialists were emergency physicians, internal medicine physicians, intensivists or 'speciality not described'. Five studies described LUS training, which varied from a 1-hour course to fully credentialed ultrasound education. In general, the methodological quality of studies was good, though, some studies had a high risk of bias.

CONCLUSION

We found significant heterogeneity across studies. In the majority of studies, LUS in the hands of the non-imaging specialists demonstrated high sensitivities and specificities in diagnosing pneumonia. However, due to problems with methodology and heterogeneity there is a need for larger studies with uniform and clearly established criteria for diagnosis and blinding.

PROSPERO REGISTRATION NUMBER

Prospectively registered in PROSPERO (CRD42017057804).

COVID-19: A Multimodality Review of Radiologic Techniques, Clinical Utility, and Imaging Features

In this article we will review the imaging features of coronavirus disease 2019 (COVID-19) across multiple modalities, including radiography, CT, MRI, PET/CT, and US. Given that COVID-19 primarily affects the lung parenchyma by causing pneumonia, our directive is to focus on thoracic findings associated with COVID-19. We aim to enhance radiologists' understanding of this disease to help guide diagnosis and management. Supplemental material is available for this article. © RSNA, 2020.